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c6c22dae24614224ae1f25a6c2fee18fd300b7ae | # Dataset Card for "oeis"
Work in Progress.
Data source: daily dump from March 28th, 2023.
OEIS End-User License: http://oeis.org/LICENSE | cakiki/oeis | [
"size_categories:100K<n<1M",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-03-29T14:15:43+00:00 | {"license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "On-Line Encyclopedia of Integer Sequences Dataset", "dataset_info": {"features": [{"name": "a-number", "dtype": "string"}, {"name": "sequence", "sequence": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 155208770, "num_examples": 361596}], "download_size": 69958943, "dataset_size": 155208770}} | 2023-03-29T14:43:56+00:00 | []
| []
| TAGS
#size_categories-100K<n<1M #license-cc-by-sa-4.0 #region-us
| # Dataset Card for "oeis"
Work in Progress.
Data source: daily dump from March 28th, 2023.
OEIS End-User License: URL | [
"# Dataset Card for \"oeis\"\n\nWork in Progress.\n\nData source: daily dump from March 28th, 2023.\nOEIS End-User License: URL"
]
| [
"TAGS\n#size_categories-100K<n<1M #license-cc-by-sa-4.0 #region-us \n",
"# Dataset Card for \"oeis\"\n\nWork in Progress.\n\nData source: daily dump from March 28th, 2023.\nOEIS End-User License: URL"
]
| [
29,
37
]
| [
"passage: TAGS\n#size_categories-100K<n<1M #license-cc-by-sa-4.0 #region-us \n# Dataset Card for \"oeis\"\n\nWork in Progress.\n\nData source: daily dump from March 28th, 2023.\nOEIS End-User License: URL"
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|
5e9ad453cb11d18f026eed66394d2c734fe5365c | # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T14:25:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 692068, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 1329559, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1330297, "num_examples": 1880}], "download_size": 997973, "dataset_size": 3351924}} | 2023-03-29T14:37:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
46
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
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|
6c8fb21189edfe433d92558236e767aef5f72413 |
# PyCoder
This repository contains the dataset for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673)
The sample code to run the model can be found in directory: "`assets/notebooks/inference.ipynb`" in our GitHub: https://github.com/awsm-research/pycoder.
PyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively
learn the code prediction task and the type prediction task. For the type prediction
task, we propose to leverage the standard Python token
type information (e.g., String, Number, Name, Keyword),
which is readily available and lightweight, instead of using
the AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper).
More information can be found in our paper.
If you use our code or PyCoder, please cite our paper.
<pre><code>@article{takerngsaksiri2022syntax,
title={Syntax-Aware On-the-Fly Code Completion},
author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
journal={arXiv preprint arXiv:2211.04673},
year={2022}
}</code></pre> | Wannita/PyCoder-Type | [
"task_categories:text-generation",
"license:mit",
"code",
"arxiv:2211.04673",
"region:us"
]
| 2023-03-29T14:26:00+00:00 | {"license": "mit", "task_categories": ["text-generation"], "pretty_name": "pycoder-type", "tags": ["code"]} | 2023-03-29T14:51:09+00:00 | [
"2211.04673"
]
| []
| TAGS
#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us
|
# PyCoder
This repository contains the dataset for the paper Syntax-Aware On-the-Fly Code Completion
The sample code to run the model can be found in directory: "'assets/notebooks/URL'" in our GitHub: URL
PyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively
learn the code prediction task and the type prediction task. For the type prediction
task, we propose to leverage the standard Python token
type information (e.g., String, Number, Name, Keyword),
which is readily available and lightweight, instead of using
the AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper).
More information can be found in our paper.
If you use our code or PyCoder, please cite our paper.
<pre><code>@article{takerngsaksiri2022syntax,
title={Syntax-Aware On-the-Fly Code Completion},
author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
journal={arXiv preprint arXiv:2211.04673},
year={2022}
}</code></pre> | [
"# PyCoder\n\nThis repository contains the dataset for the paper Syntax-Aware On-the-Fly Code Completion\n\nThe sample code to run the model can be found in directory: \"'assets/notebooks/URL'\" in our GitHub: URL\n\nPyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively\nlearn the code prediction task and the type prediction task. For the type prediction\ntask, we propose to leverage the standard Python token\ntype information (e.g., String, Number, Name, Keyword),\nwhich is readily available and lightweight, instead of using\nthe AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper). \n\nMore information can be found in our paper.\n\nIf you use our code or PyCoder, please cite our paper.\n\n<pre><code>@article{takerngsaksiri2022syntax,\n title={Syntax-Aware On-the-Fly Code Completion},\n author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},\n journal={arXiv preprint arXiv:2211.04673},\n year={2022}\n}</code></pre>"
]
| [
"TAGS\n#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us \n",
"# PyCoder\n\nThis repository contains the dataset for the paper Syntax-Aware On-the-Fly Code Completion\n\nThe sample code to run the model can be found in directory: \"'assets/notebooks/URL'\" in our GitHub: URL\n\nPyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively\nlearn the code prediction task and the type prediction task. For the type prediction\ntask, we propose to leverage the standard Python token\ntype information (e.g., String, Number, Name, Keyword),\nwhich is readily available and lightweight, instead of using\nthe AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper). \n\nMore information can be found in our paper.\n\nIf you use our code or PyCoder, please cite our paper.\n\n<pre><code>@article{takerngsaksiri2022syntax,\n title={Syntax-Aware On-the-Fly Code Completion},\n author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},\n journal={arXiv preprint arXiv:2211.04673},\n year={2022}\n}</code></pre>"
]
| [
33,
313
]
| [
"passage: TAGS\n#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us \n# PyCoder\n\nThis repository contains the dataset for the paper Syntax-Aware On-the-Fly Code Completion\n\nThe sample code to run the model can be found in directory: \"'assets/notebooks/URL'\" in our GitHub: URL\n\nPyCoder is an auto code completion model which leverages a Multi-Task Training technique (MTT) to cooperatively\nlearn the code prediction task and the type prediction task. For the type prediction\ntask, we propose to leverage the standard Python token\ntype information (e.g., String, Number, Name, Keyword),\nwhich is readily available and lightweight, instead of using\nthe AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper). \n\nMore information can be found in our paper.\n\nIf you use our code or PyCoder, please cite our paper.\n\n<pre><code>@article{takerngsaksiri2022syntax,\n title={Syntax-Aware On-the-Fly Code Completion},\n author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},\n journal={arXiv preprint arXiv:2211.04673},\n year={2022}\n}</code></pre>"
]
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|
5218f33557b4e96fb203c3103a4a130bb2bca3e3 | # Dataset Card for "balanced_augmented_dataset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jsevisal/balanced_augmented_dataset_2 | [
"region:us"
]
| 2023-03-29T14:28:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "gestures", "sequence": "string"}, {"name": "label", "sequence": {"class_label": {"names": {"0": "B-BUT", "1": "I-BUT", "2": "B-CALM_DOWN", "3": "I-CALM_DOWN", "4": "B-COME_ON", "5": "I-COME_ON", "6": "B-EMPHATIC", "7": "I-EMPHATIC", "8": "B-ENTHUSIASTIC", "9": "I-ENTHUSIASTIC", "10": "B-EXPLAIN", "11": "I-EXPLAIN", "12": "B-FRONT", "13": "I-FRONT", "14": "B-GREET", "15": "I-GREET", "16": "B-ITERATE", "17": "I-ITERATE", "18": "B-NEUTRAL", "19": "I-NEUTRAL", "20": "B-NO", "21": "I-NO", "22": "B-NO_GESTURE", "23": "I-NO_GESTURE", "24": "B-OTHER_PEER", "25": "I-OTHER_PEER", "26": "B-PLEASE", "27": "I-PLEASE", "28": "B-QUESTION", "29": "I-QUESTION", "30": "B-SELF", "31": "I-SELF", "32": "B-SORRY", "33": "I-SORRY", "34": "B-THANKS", "35": "I-THANKS", "36": "B-THINKING", "37": "I-THINKING", "38": "B-THIRD_PERSON", "39": "I-THIRD_PERSON", "40": "B-YES", "41": "I-YES"}}}}], "splits": [{"name": "train", "num_bytes": 272426.0, "num_examples": 831}, {"name": "test", "num_bytes": 55785.0, "num_examples": 126}], "download_size": 58436, "dataset_size": 328211.0}} | 2023-09-14T10:32:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "balanced_augmented_dataset_2"
More Information needed | [
"# Dataset Card for \"balanced_augmented_dataset_2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"balanced_augmented_dataset_2\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"balanced_augmented_dataset_2\"\n\nMore Information needed"
]
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|
7f8f16cd7c452792a1fcff6d45b9ad22888083c4 | # Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669 | [
"region:us"
]
| 2023-03-29T15:03:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 984100, "num_examples": 3669}], "download_size": 217354, "dataset_size": 984100}} | 2023-03-29T15:03:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
6,
41
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
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64c9d305fcec98ef04f1172e9f949151d6774b6f | # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669 | [
"region:us"
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| 2023-03-29T15:08:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 987114, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1553605, "num_examples": 3669}], "download_size": 235457, "dataset_size": 2540719}} | 2023-04-03T21:10:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
6,
42
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
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|
1132072e6b499e5d496dbb7207e4bdc4a903b98a | # NBFI
The [NBFI dataset](https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset) from the [Kaggle](https://www.kaggle.com/datasets).
Client default prediction.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| default | Binary classification | Has the client defaulted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/nbfi")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------------------|---------------|
|`income` | `float32` |
|`owns_a_car` | `bool` |
|`owns_a_bike` | `bool` |
|`has_an_active_loan` | `bool` |
|`owns_a_house` | `bool` |
|`nr_children` | `int8` |
|`credit` | `float32` |
|`loan_annuity` | `float32` |
|`accompanied_by` | `string` |
|`income_type` | `string` |
|`education_level` | `float32` |
|`marital_status` | `float32` |
|`is_male` | `bool` |
|`type_of_contract` | `string` |
|`type_of_housing` | `string` |
|`residence_density` | `float32` |
|`age_in_days` | `int32` |
|`consecutive_days_of_employment` | `int16` |
|`nr_days_since_last_registration_change` | `int32` |
|`nr_days_since_last_document_change` | `int32` |
|`owned_a_house_for_nr_days` | `int32` |
|`has_provided_a_mobile_number` | `bool` |
|`has_provided_a_home_number` | `bool` |
|`was_reachable_at_work` | `bool` |
|`job` | `string` |
|`nr_family_members` | `int8` |
|`city_rating` | `int8` |
|`weekday_of_application` | `int8` |
|`hour_of_application` | `float32` |
|`same_residence_and_home` | `bool` |
|`same_work_and_home` | `bool` |
|`score_1` | `float32` |
|`score_2` | `float32` |
|`score_3` | `float32` |
|`nr_defaults_in_social_circle` | `int8` |
|`inquiries_in_last_year` | `float32` | | mstz/nbfi | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"nbfi",
"tabular_classification",
"binary_classification",
"region:us"
]
| 2023-03-29T15:21:38+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "NBFI", "tags": ["nbfi", "tabular_classification", "binary_classification"], "configs": ["default"]} | 2024-01-19T16:12:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #nbfi #tabular_classification #binary_classification #region-us
| NBFI
====
The NBFI dataset from the Kaggle.
Client default prediction.
Configuration: default, Task: Binary classification, Description: Has the client defaulted?
Usage
=====
Features
========
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #nbfi #tabular_classification #binary_classification #region-us \n"
]
| [
55
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #nbfi #tabular_classification #binary_classification #region-us \n"
]
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|
865180372fc40249b38c962c2dfc462fd66c9b4b |
# scientific_lay_summarisation - PLOS - normalized
This dataset is a modified version of [tomasg25/scientific_lay_summarization](https://huggingface.co/datasets/tomasg25/scientific_lay_summarisation) and contains scientific lay summaries that have been preprocessed [with this code](https://gist.github.com/pszemraj/bd344637af7c0c10ecf4ab62c4d0ce91). The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.
Original dataset details:
- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
## Data Cleaning
The text in both the "article" and "summary" columns was processed to ensure that punctuation and whitespace were consistent. The `fix_punct_whitespace` function was applied to each text sample to:
- Remove spaces before punctuation marks (except for parentheses)
- Add a space after punctuation marks (except for parentheses) if missing
- Handle spaces around parentheses
- Add a space after a closing parenthesis if followed by a word or opening parenthesis
- Handle spaces around quotation marks
- Handle spaces around single quotes
- Handle comma in numbers
## Tokenization
The length of each text sample was calculated in terms of tokens using the T5 tokenizer. The `calculate_token_length` function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.
## Data Format
The resulting processed data files are stored in Apache parquet and can be loaded using the `pandas' library or the `datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are
```python
DatasetDict({
train: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 24773
})
test: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 1376
})
validation: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 1376
})
})
```
## Usage
Load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`:
```python
# download the dataset files by clicking on 'use in datasets' and cloning
import pandas as pd
# Load train set
df = pd.read_parquet("scientific_lay_summarisation-plos-norm/train.parquet")
print(df.info())
```
And here is an example using `datasets`:
```python
from datasets import load_dataset
dataset = load_dataset("pszemraj/scientific_lay_summarisation-plos-norm")
train_set = dataset['train']
# Print the first few samples
for i in range(5):
print(train_set[i])
```
## Token Lengths
For train split:

---
| pszemraj/scientific_lay_summarisation-plos-norm | [
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:tomasg25/scientific_lay_summarisation",
"language:en",
"license:mit",
"arxiv:2210.09932",
"region:us"
]
| 2023-03-29T15:24:26+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": "tomasg25/scientific_lay_summarisation", "task_categories": ["summarization", "text2text-generation"]} | 2023-06-20T00:06:39+00:00 | [
"2210.09932"
]
| [
"en"
]
| TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #arxiv-2210.09932 #region-us
|
# scientific_lay_summarisation - PLOS - normalized
This dataset is a modified version of tomasg25/scientific_lay_summarization and contains scientific lay summaries that have been preprocessed with this code. The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.
Original dataset details:
- Repository: URL
- Paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
## Data Cleaning
The text in both the "article" and "summary" columns was processed to ensure that punctuation and whitespace were consistent. The 'fix_punct_whitespace' function was applied to each text sample to:
- Remove spaces before punctuation marks (except for parentheses)
- Add a space after punctuation marks (except for parentheses) if missing
- Handle spaces around parentheses
- Add a space after a closing parenthesis if followed by a word or opening parenthesis
- Handle spaces around quotation marks
- Handle spaces around single quotes
- Handle comma in numbers
## Tokenization
The length of each text sample was calculated in terms of tokens using the T5 tokenizer. The 'calculate_token_length' function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.
## Data Format
The resulting processed data files are stored in Apache parquet and can be loaded using the 'pandas' library or the 'datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are
## Usage
Load the desired parquet file(s) using 'pandas' or 'datasets'. Here is an example using 'pandas':
And here is an example using 'datasets':
## Token Lengths
For train split:
!train-lengths
---
| [
"# scientific_lay_summarisation - PLOS - normalized\n\nThis dataset is a modified version of tomasg25/scientific_lay_summarization and contains scientific lay summaries that have been preprocessed with this code. The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.\n\nOriginal dataset details:\n- Repository: URL\n- Paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature",
"## Data Cleaning\n\nThe text in both the \"article\" and \"summary\" columns was processed to ensure that punctuation and whitespace were consistent. The 'fix_punct_whitespace' function was applied to each text sample to:\n- Remove spaces before punctuation marks (except for parentheses)\n- Add a space after punctuation marks (except for parentheses) if missing\n- Handle spaces around parentheses\n- Add a space after a closing parenthesis if followed by a word or opening parenthesis\n- Handle spaces around quotation marks\n- Handle spaces around single quotes\n- Handle comma in numbers",
"## Tokenization\n\nThe length of each text sample was calculated in terms of tokens using the T5 tokenizer. The 'calculate_token_length' function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.",
"## Data Format\n\nThe resulting processed data files are stored in Apache parquet and can be loaded using the 'pandas' library or the 'datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are",
"## Usage\n\nLoad the desired parquet file(s) using 'pandas' or 'datasets'. Here is an example using 'pandas':\n\n\n\n\n\nAnd here is an example using 'datasets':",
"## Token Lengths\n\n\nFor train split:\n\n!train-lengths\n\n---"
]
| [
"TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #arxiv-2210.09932 #region-us \n",
"# scientific_lay_summarisation - PLOS - normalized\n\nThis dataset is a modified version of tomasg25/scientific_lay_summarization and contains scientific lay summaries that have been preprocessed with this code. The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.\n\nOriginal dataset details:\n- Repository: URL\n- Paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature",
"## Data Cleaning\n\nThe text in both the \"article\" and \"summary\" columns was processed to ensure that punctuation and whitespace were consistent. The 'fix_punct_whitespace' function was applied to each text sample to:\n- Remove spaces before punctuation marks (except for parentheses)\n- Add a space after punctuation marks (except for parentheses) if missing\n- Handle spaces around parentheses\n- Add a space after a closing parenthesis if followed by a word or opening parenthesis\n- Handle spaces around quotation marks\n- Handle spaces around single quotes\n- Handle comma in numbers",
"## Tokenization\n\nThe length of each text sample was calculated in terms of tokens using the T5 tokenizer. The 'calculate_token_length' function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.",
"## Data Format\n\nThe resulting processed data files are stored in Apache parquet and can be loaded using the 'pandas' library or the 'datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are",
"## Usage\n\nLoad the desired parquet file(s) using 'pandas' or 'datasets'. Here is an example using 'pandas':\n\n\n\n\n\nAnd here is an example using 'datasets':",
"## Token Lengths\n\n\nFor train split:\n\n!train-lengths\n\n---"
]
| [
80,
123,
146,
82,
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]
| [
"passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #arxiv-2210.09932 #region-us \n# scientific_lay_summarisation - PLOS - normalized\n\nThis dataset is a modified version of tomasg25/scientific_lay_summarization and contains scientific lay summaries that have been preprocessed with this code. The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.\n\nOriginal dataset details:\n- Repository: URL\n- Paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature## Data Cleaning\n\nThe text in both the \"article\" and \"summary\" columns was processed to ensure that punctuation and whitespace were consistent. The 'fix_punct_whitespace' function was applied to each text sample to:\n- Remove spaces before punctuation marks (except for parentheses)\n- Add a space after punctuation marks (except for parentheses) if missing\n- Handle spaces around parentheses\n- Add a space after a closing parenthesis if followed by a word or opening parenthesis\n- Handle spaces around quotation marks\n- Handle spaces around single quotes\n- Handle comma in numbers## Tokenization\n\nThe length of each text sample was calculated in terms of tokens using the T5 tokenizer. The 'calculate_token_length' function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.## Data Format\n\nThe resulting processed data files are stored in Apache parquet and can be loaded using the 'pandas' library or the 'datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are"
]
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|
40dd102d8f4e53439b81a71728cd3c64764dc94c |
# scientific_lay_summarisation - elife - normalized
This is the "_elife_" split. For more words, refer to the [PLOS split README](https://huggingface.co/datasets/pszemraj/scientific_lay_summarisation-plos-norm)
## Contents
load with datasets:
```python
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("pszemraj/scientific_lay_summarisation-elife-norm")
dataset
```
Output:
```python
DatasetDict({
train: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 4346
})
test: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
validation: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
})
```
## Lengths
Train set:

| pszemraj/scientific_lay_summarisation-elife-norm | [
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:tomasg25/scientific_lay_summarisation",
"language:en",
"license:mit",
"region:us"
]
| 2023-03-29T15:26:37+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": "tomasg25/scientific_lay_summarisation", "task_categories": ["summarization", "text2text-generation"]} | 2023-04-06T22:34:11+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us
|
# scientific_lay_summarisation - elife - normalized
This is the "_elife_" split. For more words, refer to the PLOS split README
## Contents
load with datasets:
Output:
## Lengths
Train set:
!t5-tokens
| [
"# scientific_lay_summarisation - elife - normalized\n\nThis is the \"_elife_\" split. For more words, refer to the PLOS split README",
"## Contents \n\nload with datasets:\n\n\nOutput:",
"## Lengths \n\nTrain set:\n\n!t5-tokens"
]
| [
"TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us \n",
"# scientific_lay_summarisation - elife - normalized\n\nThis is the \"_elife_\" split. For more words, refer to the PLOS split README",
"## Contents \n\nload with datasets:\n\n\nOutput:",
"## Lengths \n\nTrain set:\n\n!t5-tokens"
]
| [
71,
37,
12,
12
]
| [
"passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us \n# scientific_lay_summarisation - elife - normalized\n\nThis is the \"_elife_\" split. For more words, refer to the PLOS split README## Contents \n\nload with datasets:\n\n\nOutput:## Lengths \n\nTrain set:\n\n!t5-tokens"
]
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43de0f71ba24c93c64d82452e93ea7f2f293143c | # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669 | [
"region:us"
]
| 2023-03-29T15:36:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 466723, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 248812, "num_examples": 3669}], "download_size": 61769, "dataset_size": 715535}} | 2023-04-03T19:57:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
6,
38
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
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|
1ab52e117f33cfd7d78b6a3448537e61d1a1377c | # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669 | [
"region:us"
]
| 2023-03-29T15:49:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1065554, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535372, "num_examples": 3669}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2956478, "num_examples": 3669}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5797536, "num_examples": 3669}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 1526722, "num_examples": 3669}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2808638, "num_examples": 3669}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2936255, "num_examples": 3669}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5508214, "num_examples": 3669}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1469085, "num_examples": 3669}], "download_size": 3384830, "dataset_size": 25603854}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]} | 2024-01-30T03:49:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
]
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2c7a56f6fd8654a367c916e61c0fa3ef390824ec | # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669 | [
"region:us"
]
| 2023-03-29T15:54:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1065643, "num_examples": 3669}], "download_size": 193852, "dataset_size": 1065643}} | 2023-03-29T15:55:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669\"\n\nMore Information needed"
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|
48fedd9b3db57f7d3d0ae0cd05084ca37210248f | # Dataset Card for "somos-alpaca-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | monicaeme/somos-alpaca-es | [
"region:us"
]
| 2023-03-29T16:24:49+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 1920697, "num_examples": 102}], "download_size": 0, "dataset_size": 1920697}} | 2023-04-04T11:07:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-alpaca-es"
More Information needed | [
"# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
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|
4920d6c924eda9058071deba952910f560529a62 | # Dataset Card for "tomatoesTest2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesTest2 | [
"region:us"
]
| 2023-03-29T16:37:36+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 174243.0, "num_examples": 1}], "download_size": 23284, "dataset_size": 174243.0}} | 2023-03-29T16:37:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesTest2"
More Information needed | [
"# Dataset Card for \"tomatoesTest2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesTest2\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesTest2\"\n\nMore Information needed"
]
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|
c357d758e79fe9e21f188f71ad6171854e504850 | # Dataset Card for "tomatoesTest3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesTest3 | [
"region:us"
]
| 2023-03-29T16:40:13+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "test_labels", "2": "train", "3": "train_labels", "4": "val", "5": "val_labels"}}}}], "splits": [{"name": "train", "num_bytes": 41561872.854, "num_examples": 1114}, {"name": "test", "num_bytes": 4715509.0, "num_examples": 136}, {"name": "validation", "num_bytes": 12654111.0, "num_examples": 366}], "download_size": 51718608, "dataset_size": 58931492.854}} | 2023-03-29T16:40:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesTest3"
More Information needed | [
"# Dataset Card for \"tomatoesTest3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesTest3\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesTest3\"\n\nMore Information needed"
]
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]
|
7b44158a39c9968eee25de59780cb0bdc3474193 |
# NASA Earth Instagram
This dataset is a moderately curated subset of the posts shown on [NASA Earth's Instagram](https://www.instagram.com/nasaearth/), with an emphasis
on finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.
This dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,
to be better equipped for describing the exact pheonomena in satellite imagery.
The owner of all images and text in this data is NASA. | nkasmanoff/nasa_earth_instagram | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"size_categories:n<1K",
"region:us"
]
| 2023-03-29T16:45:06+00:00 | {"size_categories": ["n<1K"], "task_categories": ["image-to-text", "text-to-image"]} | 2023-03-30T10:04:45+00:00 | []
| []
| TAGS
#task_categories-image-to-text #task_categories-text-to-image #size_categories-n<1K #region-us
|
# NASA Earth Instagram
This dataset is a moderately curated subset of the posts shown on NASA Earth's Instagram, with an emphasis
on finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.
This dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,
to be better equipped for describing the exact pheonomena in satellite imagery.
The owner of all images and text in this data is NASA. | [
"# NASA Earth Instagram\n\nThis dataset is a moderately curated subset of the posts shown on NASA Earth's Instagram, with an emphasis \non finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.\n\nThis dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,\nto be better equipped for describing the exact pheonomena in satellite imagery. \n\nThe owner of all images and text in this data is NASA."
]
| [
"TAGS\n#task_categories-image-to-text #task_categories-text-to-image #size_categories-n<1K #region-us \n",
"# NASA Earth Instagram\n\nThis dataset is a moderately curated subset of the posts shown on NASA Earth's Instagram, with an emphasis \non finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.\n\nThis dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,\nto be better equipped for describing the exact pheonomena in satellite imagery. \n\nThe owner of all images and text in this data is NASA."
]
| [
40,
125
]
| [
"passage: TAGS\n#task_categories-image-to-text #task_categories-text-to-image #size_categories-n<1K #region-us \n# NASA Earth Instagram\n\nThis dataset is a moderately curated subset of the posts shown on NASA Earth's Instagram, with an emphasis \non finding image-text pairs where the text associated is as close as possible to being a direct caption of the image in question.\n\nThis dataset has a variety of use cases, but the one which it is originally intended for is to provide a fine-tuning dataset for image captioning models,\nto be better equipped for describing the exact pheonomena in satellite imagery. \n\nThe owner of all images and text in this data is NASA."
]
| [
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|
5e026a5d7859bb599f9b2b90342777358dfeab8d | # Dataset Card for "msp5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp5 | [
"region:us"
]
| 2023-03-29T16:46:41+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6274340, "num_examples": 3295}], "download_size": 2765581, "dataset_size": 6274340}} | 2023-03-29T16:46:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp5"
More Information needed | [
"# Dataset Card for \"msp5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp5\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp5\"\n\nMore Information needed"
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|
b9b6f64bdc2dae029fb0eb46f270e3fa88f6ffcd | # Dataset Card for "flores200_packed2_mix_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bri25yu/flores200_packed2_mix_mt5 | [
"region:us"
]
| 2023-03-29T16:48:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 13594840169, "num_examples": 10240000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 6189830203, "dataset_size": 13606338205}} | 2023-03-29T17:13:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flores200_packed2_mix_mt5"
More Information needed | [
"# Dataset Card for \"flores200_packed2_mix_mt5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flores200_packed2_mix_mt5\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flores200_packed2_mix_mt5\"\n\nMore Information needed"
]
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]
|
b0d31f1a294e6a828b72b888ecbea3453b5731a7 | # Canny DiffusionDB
This dataset is the [DiffusionDB dataset](https://huggingface.co/datasets/poloclub/diffusiondb) that is transformed using Canny transformation.
You can see samples below 👇
**Sample:**
Original Image:

Transformed Image:

Caption:
"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh "
Below you can find a small script used to create this dataset:
```python
def canny_convert(image):
image_array = np.array(image)
gray_image = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray_image, 100, 200)
edge_image = Image.fromarray(edges)
return edge_image
dataset = load_dataset("poloclub/diffusiondb", split = "train")
dataset_list = []
for data in dataset:
image_path = data["image"]
prompt = data["prompt"]
transformed_image_path = canny_convert(image_path)
new_data = {
"original_image": image,
"prompt": prompt,
"transformed_image": transformed_image,
}
dataset_list.append(new_data)
``` | jax-diffusers-event/canny_diffusiondb | [
"region:us"
]
| 2023-03-29T17:27:15+00:00 | {"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "transformed_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 604990210.0, "num_examples": 994}], "download_size": 604849707, "dataset_size": 604990210.0}} | 2023-03-29T18:36:28+00:00 | []
| []
| TAGS
#region-us
| # Canny DiffusionDB
This dataset is the DiffusionDB dataset that is transformed using Canny transformation.
You can see samples below
Sample:
Original Image:
!image
Transformed Image:
!image
Caption:
"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh "
Below you can find a small script used to create this dataset:
| [
"# Canny DiffusionDB \n\nThis dataset is the DiffusionDB dataset that is transformed using Canny transformation.\n\nYou can see samples below \n\nSample:\n\nOriginal Image:\n!image\nTransformed Image:\n!image\nCaption:\t\n\"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh \"\n\nBelow you can find a small script used to create this dataset:"
]
| [
"TAGS\n#region-us \n",
"# Canny DiffusionDB \n\nThis dataset is the DiffusionDB dataset that is transformed using Canny transformation.\n\nYou can see samples below \n\nSample:\n\nOriginal Image:\n!image\nTransformed Image:\n!image\nCaption:\t\n\"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh \"\n\nBelow you can find a small script used to create this dataset:"
]
| [
6,
138
]
| [
"passage: TAGS\n#region-us \n# Canny DiffusionDB \n\nThis dataset is the DiffusionDB dataset that is transformed using Canny transformation.\n\nYou can see samples below \n\nSample:\n\nOriginal Image:\n!image\nTransformed Image:\n!image\nCaption:\t\n\"a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh \"\n\nBelow you can find a small script used to create this dataset:"
]
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|
3a7f5b8e0b0e42aef89fdee1257d534e252a7765 | # Dataset Card for "markpoulierart"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ossaili/markpoulierart | [
"region:us"
]
| 2023-03-29T18:00:20+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 273071208.65, "num_examples": 1285}], "download_size": 282892921, "dataset_size": 273071208.65}} | 2023-09-24T19:58:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "markpoulierart"
More Information needed | [
"# Dataset Card for \"markpoulierart\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"markpoulierart\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"markpoulierart\"\n\nMore Information needed"
]
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|
2623232238645d35e5ed91e319c3643411aecd0c | # Dataset Card for "chatdoctor200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mrm8488/chatdoctor200k | [
"region:us"
]
| 2023-03-29T18:57:01+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 207526604, "num_examples": 207407}], "download_size": 115879622, "dataset_size": 207526604}} | 2023-03-29T18:57:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chatdoctor200k"
More Information needed | [
"# Dataset Card for \"chatdoctor200k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chatdoctor200k\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chatdoctor200k\"\n\nMore Information needed"
]
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|
3c1a26d43d75bd5398aca8a371fce2f2e1d04db6 | # Dataset Card for "tomatoesTest4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesTest4 | [
"region:us"
]
| 2023-03-29T18:58:02+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "labels", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}], "splits": [{"name": "train", "num_bytes": 352830773.0, "num_examples": 557}], "download_size": 51228407, "dataset_size": 352830773.0}} | 2023-03-29T18:58:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesTest4"
More Information needed | [
"# Dataset Card for \"tomatoesTest4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesTest4\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesTest4\"\n\nMore Information needed"
]
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|
6200574205231562746a3b60d4da3355fd60675a | # Dataset Card for "tomatoesTest5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesTest5 | [
"region:us"
]
| 2023-03-29T18:59:58+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}], "splits": [{"name": "train", "num_bytes": 352830773.0, "num_examples": 557}], "download_size": 51228401, "dataset_size": 352830773.0}} | 2023-03-29T19:00:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesTest5"
More Information needed | [
"# Dataset Card for \"tomatoesTest5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesTest5\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesTest5\"\n\nMore Information needed"
]
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|
b96baf4883cd1f461d9a2f3355afd91a7a9d7570 | # Dataset Card for "swedish-catalog-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Djarnis/swedish-catalog-data | [
"region:us"
]
| 2023-03-29T19:02:32+00:00 | {"dataset_info": {"features": [{"name": "layout", "dtype": "string"}, {"name": "catalog_id", "dtype": "string"}, {"name": "page", "dtype": "int64"}, {"name": "font_name", "dtype": "string"}, {"name": "size", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65421344, "num_examples": 585721}], "download_size": 5137853, "dataset_size": 65421344}} | 2023-03-29T19:03:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "swedish-catalog-data"
More Information needed | [
"# Dataset Card for \"swedish-catalog-data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"swedish-catalog-data\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"swedish-catalog-data\"\n\nMore Information needed"
]
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|
8715dc45262bfa7b746cb3005abf07cf47296110 |
## Dataset Multi30k: English-Ukrainian variation
Multi30K dataset is designed to develop multilingual multimodal researches.
Initially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.
We present a variation of this dataset manually translated for Ukrainian language.
Paper:
```python
@inproceedings{saichyshyna-etal-2023-extension,
title = "Extension {M}ulti30{K}: Multimodal Dataset for Integrated Vision and Language Research in {U}krainian",
author = "Saichyshyna, Nataliia and
Maksymenko, Daniil and
Turuta, Oleksii and
Yerokhin, Andriy and
Babii, Andrii and
Turuta, Olena",
booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.unlp-1.7",
pages = "54--61",
abstract = "We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings.The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.",
}
``` | turuta/Multi30k-uk | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:uk",
"language:en",
"license:unknown",
"common",
"multi30k",
"ukrainian",
"region:us"
]
| 2023-03-29T19:26:58+00:00 | {"language": ["uk", "en"], "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["translation", "text-generation"], "pretty_name": "ukr-multi30k", "tags": ["common", "multi30k", "ukrainian"]} | 2023-05-04T18:11:45+00:00 | []
| [
"uk",
"en"
]
| TAGS
#task_categories-translation #task_categories-text-generation #size_categories-10K<n<100K #language-Ukrainian #language-English #license-unknown #common #multi30k #ukrainian #region-us
|
## Dataset Multi30k: English-Ukrainian variation
Multi30K dataset is designed to develop multilingual multimodal researches.
Initially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.
We present a variation of this dataset manually translated for Ukrainian language.
Paper:
| [
"## Dataset Multi30k: English-Ukrainian variation\n\nMulti30K dataset is designed to develop multilingual multimodal researches.\n\nInitially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.\n\nWe present a variation of this dataset manually translated for Ukrainian language.\n\nPaper:"
]
| [
"TAGS\n#task_categories-translation #task_categories-text-generation #size_categories-10K<n<100K #language-Ukrainian #language-English #license-unknown #common #multi30k #ukrainian #region-us \n",
"## Dataset Multi30k: English-Ukrainian variation\n\nMulti30K dataset is designed to develop multilingual multimodal researches.\n\nInitially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.\n\nWe present a variation of this dataset manually translated for Ukrainian language.\n\nPaper:"
]
| [
67,
100
]
| [
"passage: TAGS\n#task_categories-translation #task_categories-text-generation #size_categories-10K<n<100K #language-Ukrainian #language-English #license-unknown #common #multi30k #ukrainian #region-us \n## Dataset Multi30k: English-Ukrainian variation\n\nMulti30K dataset is designed to develop multilingual multimodal researches.\n\nInitially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.\n\nWe present a variation of this dataset manually translated for Ukrainian language.\n\nPaper:"
]
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|
18f0eac7a573ca920d7829999171302599e11788 | # Dataset Card for "teste"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Thiagofer/teste | [
"region:us"
]
| 2023-03-29T19:31:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1398770, "num_examples": 2659}, {"name": "validation", "num_bytes": 429875, "num_examples": 665}], "download_size": 995956, "dataset_size": 1828645}} | 2023-03-29T20:08:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "teste"
More Information needed | [
"# Dataset Card for \"teste\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"teste\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"teste\"\n\nMore Information needed"
]
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|
afb8395a583dd58e7080c2492cf71ef6734bb017 |
#pragma once
#include "Board.h"
#include "GameObjects.h"
#include "Point.h"
#include <Windows.h>
#include <conio.h>
class TetrisGame {
Board boardGame;
public:
void resetGame(){
boardGame.setBoard();
}
// <<<RUN>>>
void run();
bool checkGameOver(int typeShapea);
void updateStartBoard(int typeShape);
void hideCursor();
GameObjects * createNewObject(int & type); | bruce69/54534654 | [
"region:us"
]
| 2023-03-29T20:00:21+00:00 | {} | 2023-03-29T20:03:54+00:00 | []
| []
| TAGS
#region-us
|
#pragma once
#include "Board.h"
#include "GameObjects.h"
#include "Point.h"
#include <Windows.h>
#include <conio.h>
class TetrisGame {
Board boardGame;
public:
void resetGame(){
boardGame.setBoard();
}
// <<<RUN>>>
void run();
bool checkGameOver(int typeShapea);
void updateStartBoard(int typeShape);
void hideCursor();
GameObjects * createNewObject(int & type); | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
9ae70b9dec5e9873946b104111a0a0ce101b8002 | # Dataset Card for "segundo_harem_conll_2003_style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arubenruben/segundo_harem_conll_2003_style | [
"region:us"
]
| 2023-03-29T21:18:41+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 1047476, "num_examples": 93}, {"name": "validation", "num_bytes": 249755, "num_examples": 23}], "download_size": 295815, "dataset_size": 1297231}} | 2023-04-12T07:12:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "segundo_harem_conll_2003_style"
More Information needed | [
"# Dataset Card for \"segundo_harem_conll_2003_style\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"segundo_harem_conll_2003_style\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"segundo_harem_conll_2003_style\"\n\nMore Information needed"
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|
61f3d559eb6b9284c9b20b79a297e0fd277defd6 | # Dataset Card for "DA_SGD_Restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Restaurants | [
"region:us"
]
| 2023-03-29T21:48:55+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 895010.6571663469, "num_examples": 3648}, {"name": "test", "num_bytes": 233, "num_examples": 1}], "download_size": 360352, "dataset_size": 895243.6571663469}} | 2023-03-29T21:49:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Restaurants"
More Information needed | [
"# Dataset Card for \"DA_SGD_Restaurants\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Restaurants\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Restaurants\"\n\nMore Information needed"
]
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|
bc32eeac2eadbdb590fa0f7752f994f287cb213e | # Dataset Card for "DA_SGD_Media"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Media | [
"region:us"
]
| 2023-03-29T21:49:01+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 538641.80312262, "num_examples": 2625}, {"name": "test", "num_bytes": 199, "num_examples": 1}], "download_size": 207091, "dataset_size": 538840.80312262}} | 2023-03-29T21:49:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Media"
More Information needed | [
"# Dataset Card for \"DA_SGD_Media\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Media\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Media\"\n\nMore Information needed"
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|
94d64c0215314f5cdb0d701aab1d8402c09d3c17 | # Dataset Card for "DA_SGD_Events"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Events | [
"region:us"
]
| 2023-03-29T21:49:06+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1660456.0603351956, "num_examples": 7159}, {"name": "test", "num_bytes": 327, "num_examples": 1}], "download_size": 673850, "dataset_size": 1660783.0603351956}} | 2023-03-29T21:49:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Events"
More Information needed | [
"# Dataset Card for \"DA_SGD_Events\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Events\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Events\"\n\nMore Information needed"
]
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|
cb65aa77fd03ddbdad7447d625707745d0c0a7fc | # Dataset Card for "DA_SGD_Music"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Music | [
"region:us"
]
| 2023-03-29T21:49:11+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 628520.2361015786, "num_examples": 2913}, {"name": "test", "num_bytes": 149, "num_examples": 1}], "download_size": 241847, "dataset_size": 628669.2361015786}} | 2023-03-29T21:49:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Music"
More Information needed | [
"# Dataset Card for \"DA_SGD_Music\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Music\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Music\"\n\nMore Information needed"
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|
aebb9a14d9e193733900c86239f5bfc9a17f1529 | # Dataset Card for "DA_SGD_Movies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Movies | [
"region:us"
]
| 2023-03-29T21:49:16+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 724582.4324491055, "num_examples": 3241}, {"name": "test", "num_bytes": 241, "num_examples": 1}], "download_size": 297231, "dataset_size": 724823.4324491055}} | 2023-03-29T21:49:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Movies"
More Information needed | [
"# Dataset Card for \"DA_SGD_Movies\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Movies\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Movies\"\n\nMore Information needed"
]
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|
f62c0fe8aab865278fcf117238b4a176521e717a | # Dataset Card for "DA_SGD_Flights"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Flights | [
"region:us"
]
| 2023-03-29T21:49:21+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2067906.6395008278, "num_examples": 7852}, {"name": "test", "num_bytes": 326, "num_examples": 1}], "download_size": 791257, "dataset_size": 2068232.6395008278}} | 2023-03-29T21:49:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Flights"
More Information needed | [
"# Dataset Card for \"DA_SGD_Flights\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Flights\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Flights\"\n\nMore Information needed"
]
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|
3bcfa11507c2267a6fa23a3d9ef7095723355794 | # Dataset Card for "DA_SGD_RideSharing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_RideSharing | [
"region:us"
]
| 2023-03-29T21:49:27+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 235897.7504854369, "num_examples": 1029}, {"name": "test", "num_bytes": 196, "num_examples": 1}], "download_size": 94252, "dataset_size": 236093.7504854369}} | 2023-03-29T21:49:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_RideSharing"
More Information needed | [
"# Dataset Card for \"DA_SGD_RideSharing\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_RideSharing\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_RideSharing\"\n\nMore Information needed"
]
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|
92fd5bed52fa83528094422baa414ba196d1795a | # Dataset Card for "DA_SGD_RentalCars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_RentalCars | [
"region:us"
]
| 2023-03-29T21:49:32+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 694211.3296910324, "num_examples": 2653}, {"name": "test", "num_bytes": 404, "num_examples": 1}], "download_size": 268297, "dataset_size": 694615.3296910324}} | 2023-03-29T21:49:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_RentalCars"
More Information needed | [
"# Dataset Card for \"DA_SGD_RentalCars\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_RentalCars\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_RentalCars\"\n\nMore Information needed"
]
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|
b5105da57dda1f2fd2da55173b453be6528dbdd9 | # Dataset Card for "DA_SGD_Buses"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Buses | [
"region:us"
]
| 2023-03-29T21:49:37+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 710108.4859689666, "num_examples": 3028}, {"name": "test", "num_bytes": 221, "num_examples": 1}], "download_size": 273885, "dataset_size": 710329.4859689666}} | 2023-03-29T21:49:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Buses"
More Information needed | [
"# Dataset Card for \"DA_SGD_Buses\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Buses\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Buses\"\n\nMore Information needed"
]
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|
0984c1f2fc9a6b96861c92d180959ec050b03015 | # Dataset Card for "DA_SGD_Hotels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Hotels | [
"region:us"
]
| 2023-03-29T21:49:42+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1137138.1547469678, "num_examples": 4781}, {"name": "test", "num_bytes": 354, "num_examples": 1}], "download_size": 477647, "dataset_size": 1137492.1547469678}} | 2023-03-29T21:49:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Hotels"
More Information needed | [
"# Dataset Card for \"DA_SGD_Hotels\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Hotels\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Hotels\"\n\nMore Information needed"
]
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|
fdc46bb9fbe5122b24b84bc997b877e34cd3b80a | # Dataset Card for "DA_SGD_Services"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Services | [
"region:us"
]
| 2023-03-29T21:49:48+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1113419.0002074258, "num_examples": 4820}, {"name": "test", "num_bytes": 293, "num_examples": 1}], "download_size": 460731, "dataset_size": 1113712.0002074258}} | 2023-03-29T21:49:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Services"
More Information needed | [
"# Dataset Card for \"DA_SGD_Services\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Services\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Services\"\n\nMore Information needed"
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|
842e92501b59aaf8597256845b4db360fffb0975 | # Dataset Card for "DA_SGD_Homes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Homes | [
"region:us"
]
| 2023-03-29T21:49:53+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 782436.7041420118, "num_examples": 3041}, {"name": "test", "num_bytes": 418, "num_examples": 1}], "download_size": 311067, "dataset_size": 782854.7041420118}} | 2023-03-29T21:49:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Homes"
More Information needed | [
"# Dataset Card for \"DA_SGD_Homes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Homes\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Homes\"\n\nMore Information needed"
]
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|
d4069cfe906b00132a9c5bab716d638f5b375f22 | # Dataset Card for "DA_SGD_Banks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Banks | [
"region:us"
]
| 2023-03-29T21:49:58+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 429700.14992503746, "num_examples": 2000}, {"name": "test", "num_bytes": 228, "num_examples": 1}], "download_size": 147054, "dataset_size": 429928.14992503746}} | 2023-03-29T21:50:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Banks"
More Information needed | [
"# Dataset Card for \"DA_SGD_Banks\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Banks\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Banks\"\n\nMore Information needed"
]
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|
4524cdbcbcdfd26c4f2b1888d8d5e503e4bc12d2 | # Dataset Card for "DA_SGD_Calendar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_SGD_Calendar | [
"region:us"
]
| 2023-03-29T21:50:04+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 237228.4572815534, "num_examples": 1029}, {"name": "test", "num_bytes": 130, "num_examples": 1}], "download_size": 90115, "dataset_size": 237358.4572815534}} | 2023-03-29T21:50:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_SGD_Calendar"
More Information needed | [
"# Dataset Card for \"DA_SGD_Calendar\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_SGD_Calendar\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_SGD_Calendar\"\n\nMore Information needed"
]
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|
ceee66b545de4366590386127e7005a24fb8a54e | # Dataset Card for "tomatoesSpoof1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesSpoof1 | [
"region:us"
]
| 2023-03-29T21:53:06+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 673124095.0, "num_examples": 557}], "download_size": 34937459, "dataset_size": 673124095.0}} | 2023-03-29T21:57:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesSpoof1"
More Information needed | [
"# Dataset Card for \"tomatoesSpoof1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesSpoof1\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesSpoof1\"\n\nMore Information needed"
]
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|
96693d13afd1e79679e5d06414782b78e102ec09 | # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-polaridad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alexcom/analisis-sentimientos-textos-turisitcos-mx-polaridad | [
"region:us"
]
| 2023-03-29T22:14:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 71496873, "num_examples": 176192}, {"name": "test", "num_bytes": 30856228, "num_examples": 75510}], "download_size": 62497427, "dataset_size": 102353101}} | 2023-04-01T19:12:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-polaridad"
More Information needed | [
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-polaridad\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-polaridad\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-polaridad\"\n\nMore Information needed"
]
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|
5a64ccb93a9e27948c91dd6ba41d85f1bd64f280 | # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-tipo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alexcom/analisis-sentimeinto-textos-turisitcos-mx-tipo | [
"region:us"
]
| 2023-03-29T22:14:57+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72330945, "num_examples": 176192}, {"name": "test", "num_bytes": 31152158, "num_examples": 75510}], "download_size": 62479649, "dataset_size": 103483103}} | 2023-03-29T22:18:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-tipo"
More Information needed | [
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-tipo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-tipo\"\n\nMore Information needed"
]
| [
6,
29
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-tipo\"\n\nMore Information needed"
]
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]
|
dfbf980e1e0dee973bb7825c6507262b4689442b | # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-pais"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alexcom/analisis-sentimeinto-textos-turisitcos-mx-pais | [
"region:us"
]
| 2023-03-29T22:16:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72164531, "num_examples": 176192}, {"name": "test", "num_bytes": 30692934, "num_examples": 75510}], "download_size": 62463153, "dataset_size": 102857465}} | 2023-03-29T22:17:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-pais"
More Information needed | [
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-pais\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-pais\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"analisis-sentimeinto-textos-turisitcos-mx-pais\"\n\nMore Information needed"
]
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|
618b1378fea15e06d52d7919d328a52d4b6b8cfd | # Dataset Card for "flores200_baseline_all_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bri25yu/flores200_baseline_all_mt5 | [
"region:us"
]
| 2023-03-29T22:24:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 30474393243, "num_examples": 41287764}, {"name": "val", "num_bytes": 3791994, "num_examples": 5000}, {"name": "test", "num_bytes": 7604613, "num_examples": 10000}], "download_size": 15127185362, "dataset_size": 30485789850}} | 2023-03-29T22:50:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flores200_baseline_all_mt5"
More Information needed | [
"# Dataset Card for \"flores200_baseline_all_mt5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flores200_baseline_all_mt5\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flores200_baseline_all_mt5\"\n\nMore Information needed"
]
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|
61a9690126faa8839fff46958dbe404b11891425 | # Dataset Card for "sft_language_submix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_language_submix | [
"region:us"
]
| 2023-03-29T22:53:52+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3360236870.066389, "num_examples": 2339239}], "download_size": 1943869500, "dataset_size": 3360236870.066389}} | 2023-03-29T22:56:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_language_submix"
More Information needed | [
"# Dataset Card for \"sft_language_submix\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_language_submix\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_language_submix\"\n\nMore Information needed"
]
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|
e74e2a178161a385ecd26c5825f19e64a219e4ad | # Dataset Card for "tomatoesSpoof2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesSpoof2 | [
"region:us"
]
| 2023-03-29T23:14:39+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 673124095.0, "num_examples": 557}], "download_size": 35510907, "dataset_size": 673124095.0}} | 2023-03-29T23:17:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesSpoof2"
More Information needed | [
"# Dataset Card for \"tomatoesSpoof2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesSpoof2\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesSpoof2\"\n\nMore Information needed"
]
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|
0ebc32dbb6f12467f5fb4cb32d3c71395840bd37 | # Dataset Card for "procedural_gen_2operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/procedural_gen_2operands | [
"region:us"
]
| 2023-03-29T23:50:56+00:00 | {"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 57720, "num_examples": 2000}, {"name": "validation", "num_bytes": 11574, "num_examples": 400}], "download_size": 26153, "dataset_size": 69294}} | 2023-03-29T23:50:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "procedural_gen_2operands"
More Information needed | [
"# Dataset Card for \"procedural_gen_2operands\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"procedural_gen_2operands\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"procedural_gen_2operands\"\n\nMore Information needed"
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|
b0842720e3478f4de061b8e4e83d5c382624d0ee | # Dataset Card for "DA_MultiWOZ_hotel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_hotel | [
"region:us"
]
| 2023-03-29T23:56:11+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1627308.4499394428, "num_examples": 4953}, {"name": "test", "num_bytes": 366, "num_examples": 1}], "download_size": 653642, "dataset_size": 1627674.4499394428}} | 2023-03-29T23:56:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_hotel"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_hotel\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_hotel\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_hotel\"\n\nMore Information needed"
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|
1283a2564d3df27b7c737aeaab4f39aaf97bbef4 | # Dataset Card for "DA_MultiWOZ_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_train | [
"region:us"
]
| 2023-03-29T23:56:15+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 759964.6905864198, "num_examples": 2591}, {"name": "test", "num_bytes": 321, "num_examples": 1}], "download_size": 299705, "dataset_size": 760285.6905864198}} | 2023-03-29T23:56:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_train"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_train\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_train\"\n\nMore Information needed"
]
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|
744b019bdfa68ac2b1272c665e2b27a8fc9fba96 | # Dataset Card for "DA_MultiWOZ_restaurant"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_restaurant | [
"region:us"
]
| 2023-03-29T23:56:18+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2358714.8704030397, "num_examples": 7368}, {"name": "test", "num_bytes": 292, "num_examples": 1}], "download_size": 888220, "dataset_size": 2359006.8704030397}} | 2023-03-29T23:56:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_restaurant"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_restaurant\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_restaurant\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_restaurant\"\n\nMore Information needed"
]
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|
607b18a7746ff95d439cab5a7e3088795618ca54 | # Dataset Card for "DA_MultiWOZ_taxi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_taxi | [
"region:us"
]
| 2023-03-29T23:56:22+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 636448.1401906274, "num_examples": 2517}, {"name": "test", "num_bytes": 360, "num_examples": 1}], "download_size": 248789, "dataset_size": 636808.1401906274}} | 2023-03-29T23:56:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_taxi"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_taxi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_taxi\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_taxi\"\n\nMore Information needed"
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|
a1fab03930a80b38e4d69d51e5051b51eeebc9e3 | # Dataset Card for "DA_MultiWOZ_attraction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_attraction | [
"region:us"
]
| 2023-03-29T23:56:26+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 256994.14303638643, "num_examples": 796}, {"name": "test", "num_bytes": 390, "num_examples": 1}], "download_size": 100060, "dataset_size": 257384.14303638643}} | 2023-03-29T23:56:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_attraction"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_attraction\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_attraction\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_attraction\"\n\nMore Information needed"
]
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|
2a375160e6547c26cd81eb7759b875dc0ca802b8 | # Dataset Card for "DA_MultiWOZ_hospital"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_MultiWOZ_hospital | [
"region:us"
]
| 2023-03-29T23:56:30+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 162349.6563071298, "num_examples": 546}, {"name": "test", "num_bytes": 309, "num_examples": 1}], "download_size": 54666, "dataset_size": 162658.6563071298}} | 2023-03-29T23:56:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_MultiWOZ_hospital"
More Information needed | [
"# Dataset Card for \"DA_MultiWOZ_hospital\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_MultiWOZ_hospital\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_MultiWOZ_hospital\"\n\nMore Information needed"
]
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|
5d2720499ea96d109ad4c0c1f3a8e92f4fbaa630 | # Dataset Card for "tomatoesSpoof3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesSpoof3 | [
"region:us"
]
| 2023-03-30T00:09:25+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 37099461.0, "num_examples": 557}], "download_size": 33524817, "dataset_size": 37099461.0}} | 2023-03-30T00:12:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesSpoof3"
More Information needed | [
"# Dataset Card for \"tomatoesSpoof3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesSpoof3\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesSpoof3\"\n\nMore Information needed"
]
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|
8673d06f078e867610595b74b56a78712ae684df | # Dataset Card for "tomatoesSpoof4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesSpoof4 | [
"region:us"
]
| 2023-03-30T00:20:14+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 37049913.0, "num_examples": 557}], "download_size": 33476189, "dataset_size": 37049913.0}} | 2023-03-30T00:21:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesSpoof4"
More Information needed | [
"# Dataset Card for \"tomatoesSpoof4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesSpoof4\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesSpoof4\"\n\nMore Information needed"
]
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|
7540a209f49d8cbae9cbae03c5c0263962a78a2e | # Dataset Card for "cv_svamp_augmented_fold0_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/cv_svamp_augmented_fold0_ver2 | [
"region:us"
]
| 2023-03-30T00:30:14+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2713139, "num_examples": 7864}, {"name": "validation", "num_bytes": 162466, "num_examples": 412}], "download_size": 721267, "dataset_size": 2875605}} | 2023-03-30T02:06:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv_svamp_augmented_fold0_ver2"
More Information needed | [
"# Dataset Card for \"cv_svamp_augmented_fold0_ver2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv_svamp_augmented_fold0_ver2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold0_ver2\"\n\nMore Information needed"
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|
b3b6cb56cd8241b7f755c5e2139772046c54f10b | # Dataset Card for "cv_svamp_augmented_fold1_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/cv_svamp_augmented_fold1_ver2 | [
"region:us"
]
| 2023-03-30T00:30:18+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2716697, "num_examples": 7840}, {"name": "validation", "num_bytes": 158908, "num_examples": 436}], "download_size": 720144, "dataset_size": 2875605}} | 2023-03-30T02:06:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv_svamp_augmented_fold1_ver2"
More Information needed | [
"# Dataset Card for \"cv_svamp_augmented_fold1_ver2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv_svamp_augmented_fold1_ver2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold1_ver2\"\n\nMore Information needed"
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|
5d41b708c00594052be0ddd67e06793ee493ad69 | # Dataset Card for "cv_svamp_augmented_fold2_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/cv_svamp_augmented_fold2_ver2 | [
"region:us"
]
| 2023-03-30T00:30:22+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2712727, "num_examples": 7822}, {"name": "validation", "num_bytes": 162878, "num_examples": 454}], "download_size": 721078, "dataset_size": 2875605}} | 2023-03-30T02:06:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv_svamp_augmented_fold2_ver2"
More Information needed | [
"# Dataset Card for \"cv_svamp_augmented_fold2_ver2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv_svamp_augmented_fold2_ver2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold2_ver2\"\n\nMore Information needed"
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|
ff0464569ab4447a62d434acc4be190497df8f95 | # Dataset Card for "cv_svamp_augmented_fold3_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/cv_svamp_augmented_fold3_ver2 | [
"region:us"
]
| 2023-03-30T00:30:26+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2745859, "num_examples": 7946}, {"name": "validation", "num_bytes": 129746, "num_examples": 330}], "download_size": 720562, "dataset_size": 2875605}} | 2023-03-30T02:06:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv_svamp_augmented_fold3_ver2"
More Information needed | [
"# Dataset Card for \"cv_svamp_augmented_fold3_ver2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv_svamp_augmented_fold3_ver2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold3_ver2\"\n\nMore Information needed"
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|
fa441db4a571fbce9dbbad59a4828db785c580fd | # Dataset Card for "cv_svamp_augmented_fold4_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/cv_svamp_augmented_fold4_ver2 | [
"region:us"
]
| 2023-03-30T00:30:29+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 2745369, "num_examples": 7908}, {"name": "validation", "num_bytes": 130236, "num_examples": 368}], "download_size": 720249, "dataset_size": 2875605}} | 2023-03-30T02:06:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv_svamp_augmented_fold4_ver2"
More Information needed | [
"# Dataset Card for \"cv_svamp_augmented_fold4_ver2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv_svamp_augmented_fold4_ver2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold4_ver2\"\n\nMore Information needed"
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|
8dc0d93d62457aed48e110dccc0938aa44c7b036 | # Dataset Card for "yiyi_test_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | YiYiXu/yiyi_test_ds | [
"region:us"
]
| 2023-03-30T01:07:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "condtioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22659003.0, "num_examples": 15}], "download_size": 22663578, "dataset_size": 22659003.0}} | 2023-03-30T01:07:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "yiyi_test_ds"
More Information needed | [
"# Dataset Card for \"yiyi_test_ds\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"yiyi_test_ds\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"yiyi_test_ds\"\n\nMore Information needed"
]
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|
03766b09aede2926257bb90eabe63fbda3f241e7 | # Dataset Card for "sft_code_submix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_code_submix | [
"region:us"
]
| 2023-03-30T01:43:23+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1399630171.2578096, "num_examples": 1146098}], "download_size": 501178967, "dataset_size": 1399630171.2578096}} | 2023-03-30T01:44:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_code_submix"
More Information needed | [
"# Dataset Card for \"sft_code_submix\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_code_submix\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_code_submix\"\n\nMore Information needed"
]
| [
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|
d9b304cd64edadcb2958d708f91fea3fb0151938 | # Dataset Card for "sft_language_and_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_language_and_code | [
"region:us"
]
| 2023-03-30T01:55:29+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4754005271.891349, "num_examples": 3471810}], "download_size": 2503296527, "dataset_size": 4754005271.891349}} | 2023-03-30T01:59:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_language_and_code"
More Information needed | [
"# Dataset Card for \"sft_language_and_code\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_language_and_code\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_language_and_code\"\n\nMore Information needed"
]
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|
70349493830a9dad469fafe2710fe1147d13be98 | Pruned gpt4all dataset meant to reduce annoying behvaiors and nonsensical prompts | Nebulous/gpt4all_pruned | [
"license:cc",
"region:us"
]
| 2023-03-30T02:16:53+00:00 | {"license": "cc"} | 2023-04-03T22:29:29+00:00 | []
| []
| TAGS
#license-cc #region-us
| Pruned gpt4all dataset meant to reduce annoying behvaiors and nonsensical prompts | []
| [
"TAGS\n#license-cc #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-cc #region-us \n"
]
| [
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|
68f99de54e08637b568a8ffdbe97cdc33a2327d4 | # Dataset Card for "msp4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp4 | [
"region:us"
]
| 2023-03-30T03:20:59+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4454603, "num_examples": 2577}], "download_size": 1953609, "dataset_size": 4454603}} | 2023-03-30T03:21:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp4"
More Information needed | [
"# Dataset Card for \"msp4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp4\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp4\"\n\nMore Information needed"
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|
3d3b9ae92a10544b84de45767d138d66f1c38635 | # Dataset Card for "msp12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp12 | [
"region:us"
]
| 2023-03-30T03:29:55+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4706548, "num_examples": 2260}], "download_size": 2172589, "dataset_size": 4706548}} | 2023-03-30T03:29:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp12"
More Information needed | [
"# Dataset Card for \"msp12\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp12\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp12\"\n\nMore Information needed"
]
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|
5ad281bcb0218ef16e8beeee16693f212c2a3735 | # Dataset Card for "msp10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp10 | [
"region:us"
]
| 2023-03-30T03:37:35+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9444843, "num_examples": 7772}], "download_size": 3887624, "dataset_size": 9444843}} | 2023-03-30T03:37:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp10"
More Information needed | [
"# Dataset Card for \"msp10\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp10\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp10\"\n\nMore Information needed"
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|
99531716abbc681394e1965ed7fd8dd07e5efdc1 | # Dataset Card for "msp8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp8 | [
"region:us"
]
| 2023-03-30T03:41:41+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5451396, "num_examples": 5348}], "download_size": 2135865, "dataset_size": 5451396}} | 2023-03-30T03:41:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp8"
More Information needed | [
"# Dataset Card for \"msp8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp8\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp8\"\n\nMore Information needed"
]
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|
2f76eeccc0e255361b555ed3d44ee8d8bd4815b8 | # Dataset Card for "msp9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp9 | [
"region:us"
]
| 2023-03-30T03:46:47+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8204305, "num_examples": 6859}], "download_size": 3436668, "dataset_size": 8204305}} | 2023-03-30T03:46:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp9"
More Information needed | [
"# Dataset Card for \"msp9\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp9\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp9\"\n\nMore Information needed"
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]
|
1b97199d0a88545bf74dc69868656906c0921388 | # Dataset Card for "msp6newmodel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp6newmodel | [
"region:us"
]
| 2023-03-30T03:53:41+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4381100, "num_examples": 3079}], "download_size": 1929565, "dataset_size": 4381100}} | 2023-03-30T03:53:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp6newmodel"
More Information needed | [
"# Dataset Card for \"msp6newmodel\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp6newmodel\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp6newmodel\"\n\nMore Information needed"
]
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|
461011389de53a545206c38d1c5ec93194e81062 | # Distillation for quantization on Textual Inversion models to personalize text2image with Intel® Neural Compressor
<p float="left">
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/FP32.png" width = "300" height = "300" alt="FP32" align=center />
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/INT8.png" width = "300" height = "300" alt="INT8" align=center />
</p>
Image on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.
<br>
Please refer to this <a href="https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion/distillation_for_quantization">example</a> of Intel® Neural Compressor for more detail. | Intel/textual_inversion_dicoo_dfq | [
"license:apache-2.0",
"region:us"
]
| 2023-03-30T03:54:13+00:00 | {"license": "apache-2.0"} | 2023-03-30T04:19:52+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| # Distillation for quantization on Textual Inversion models to personalize text2image with Intel® Neural Compressor
<p float="left">
<img src="URL width = "300" height = "300" alt="FP32" align=center />
<img src="URL width = "300" height = "300" alt="INT8" align=center />
</p>
Image on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.
<br>
Please refer to this <a href="URL of Intel® Neural Compressor for more detail. | [
"# Distillation for quantization on Textual Inversion models to personalize text2image with Intel® Neural Compressor\n\n<p float=\"left\">\n <img src=\"URL width = \"300\" height = \"300\" alt=\"FP32\" align=center />\n <img src=\"URL width = \"300\" height = \"300\" alt=\"INT8\" align=center />\n</p>\n\nImage on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.\n<br>\nPlease refer to this <a href=\"URL of Intel® Neural Compressor for more detail."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Distillation for quantization on Textual Inversion models to personalize text2image with Intel® Neural Compressor\n\n<p float=\"left\">\n <img src=\"URL width = \"300\" height = \"300\" alt=\"FP32\" align=center />\n <img src=\"URL width = \"300\" height = \"300\" alt=\"INT8\" align=center />\n</p>\n\nImage on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.\n<br>\nPlease refer to this <a href=\"URL of Intel® Neural Compressor for more detail."
]
| [
14,
173
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n# Distillation for quantization on Textual Inversion models to personalize text2image with Intel® Neural Compressor\n\n<p float=\"left\">\n <img src=\"URL width = \"300\" height = \"300\" alt=\"FP32\" align=center />\n <img src=\"URL width = \"300\" height = \"300\" alt=\"INT8\" align=center />\n</p>\n\nImage on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.\n<br>\nPlease refer to this <a href=\"URL of Intel® Neural Compressor for more detail."
]
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|
a81a5d76a6f472c25a13a19cfb24773ada153b96 | # Dataset Card for "msp11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp11 | [
"region:us"
]
| 2023-03-30T04:02:58+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7371058, "num_examples": 2200}], "download_size": 3570495, "dataset_size": 7371058}} | 2023-03-30T04:03:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp11"
More Information needed | [
"# Dataset Card for \"msp11\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp11\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp11\"\n\nMore Information needed"
]
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|
98543471d49c953af6cb7bdd96af39c1e0785731 | # Dataset Card for "fewshot_multiwoz_restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/fewshot_multiwoz_restaurants | [
"region:us"
]
| 2023-03-30T04:07:22+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17398, "num_examples": 56}, {"name": "validation", "num_bytes": 4503, "num_examples": 14}, {"name": "test", "num_bytes": 2336842, "num_examples": 7298}], "download_size": 893258, "dataset_size": 2358743}} | 2023-03-30T04:07:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fewshot_multiwoz_restaurants"
More Information needed | [
"# Dataset Card for \"fewshot_multiwoz_restaurants\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fewshot_multiwoz_restaurants\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fewshot_multiwoz_restaurants\"\n\nMore Information needed"
]
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|
c6a22eaebca4315f518b571c4fb44effecaa18d3 | # Dataset Card for TransactPro FAQ!
This is a synthetic dataset made with GPT-4. | c-s-ale/transactpro-dataset | [
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
]
| 2023-03-30T04:22:43+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["table-question-answering"], "pretty_name": "TransactPro FAQ Dataset", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7808, "num_examples": 24}, {"name": "test", "num_bytes": 976, "num_examples": 3}, {"name": "valid", "num_bytes": 976, "num_examples": 3}], "download_size": 15787, "dataset_size": 9760}} | 2023-03-30T17:10:04+00:00 | []
| [
"en"
]
| TAGS
#task_categories-table-question-answering #size_categories-n<1K #language-English #license-openrail #region-us
| # Dataset Card for TransactPro FAQ!
This is a synthetic dataset made with GPT-4. | [
"# Dataset Card for TransactPro FAQ!\n\nThis is a synthetic dataset made with GPT-4."
]
| [
"TAGS\n#task_categories-table-question-answering #size_categories-n<1K #language-English #license-openrail #region-us \n",
"# Dataset Card for TransactPro FAQ!\n\nThis is a synthetic dataset made with GPT-4."
]
| [
40,
24
]
| [
"passage: TAGS\n#task_categories-table-question-answering #size_categories-n<1K #language-English #license-openrail #region-us \n# Dataset Card for TransactPro FAQ!\n\nThis is a synthetic dataset made with GPT-4."
]
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|
ac63c59a3f1f4d71f75df29bc74a4400ecc71afb | # Dataset Card for "clean-dataset-preview"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AlekseyKorshuk/clean-dataset-preview | [
"region:us"
]
| 2023-03-30T05:17:35+00:00 | {"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "check_word_number_criteria", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 686595411, "num_examples": 337253}], "download_size": 283358430, "dataset_size": 686595411}} | 2023-03-31T16:40:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clean-dataset-preview"
More Information needed | [
"# Dataset Card for \"clean-dataset-preview\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"clean-dataset-preview\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"clean-dataset-preview\"\n\nMore Information needed"
]
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|
4dd5548e08ac639ef1833dc6814606689537add5 | # Dataset Card for "diff_sitemap_and_direct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hayesyang/diff_sitemap_and_direct | [
"region:us"
]
| 2023-03-30T05:36:04+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "sitemap", "dtype": "string"}, {"name": "local", "dtype": "string"}, {"name": "quick_ratio", "dtype": "float64"}], "splits": [{"name": "zh", "num_bytes": 74903836, "num_examples": 2771}, {"name": "en", "num_bytes": 69187224, "num_examples": 2258}, {"name": "fr", "num_bytes": 38867616, "num_examples": 1201}, {"name": "es", "num_bytes": 56906331, "num_examples": 1695}, {"name": "ru", "num_bytes": 35285827, "num_examples": 926}, {"name": "ar", "num_bytes": 34554954, "num_examples": 883}], "download_size": 84893570, "dataset_size": 309705788}} | 2023-03-30T05:36:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "diff_sitemap_and_direct"
More Information needed | [
"# Dataset Card for \"diff_sitemap_and_direct\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"diff_sitemap_and_direct\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"diff_sitemap_and_direct\"\n\nMore Information needed"
]
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|
cbeba6d5958447b78046319f1b4c5a16086a72f3 | # Dataset Card for "slurp_dataset_audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yhfang/slurp_dataset_audio | [
"region:us"
]
| 2023-03-30T05:55:41+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "slurp_id", "dtype": "int64"}, {"name": "intent", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2366367342.757, "num_examples": 50627}, {"name": "test", "num_bytes": 614902079.546, "num_examples": 13078}, {"name": "validation", "num_bytes": 436133520.91, "num_examples": 8690}], "download_size": 3912246041, "dataset_size": 3417402943.213}} | 2023-03-30T06:11:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "slurp_dataset_audio"
More Information needed | [
"# Dataset Card for \"slurp_dataset_audio\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"slurp_dataset_audio\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"slurp_dataset_audio\"\n\nMore Information needed"
]
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|
a46f98219e858c49500688be011861712b6d9f31 |
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna) | Chinese-Vicuna/guanaco_belle_merge_v1.0 | [
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
]
| 2023-03-30T06:29:07+00:00 | {"language": ["zh", "en", "ja"], "license": "gpl-3.0"} | 2023-03-30T06:49:30+00:00 | []
| [
"zh",
"en",
"ja"
]
| TAGS
#language-Chinese #language-English #language-Japanese #license-gpl-3.0 #region-us
|
Thanks for Guanaco Dataset and Belle Dataset
This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code Chinese-Vicuna | []
| [
"TAGS\n#language-Chinese #language-English #language-Japanese #license-gpl-3.0 #region-us \n"
]
| [
29
]
| [
"passage: TAGS\n#language-Chinese #language-English #language-Japanese #license-gpl-3.0 #region-us \n"
]
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|
09b77e3275945c0e51d45c331b4c58c265c3cfd8 | # Dataset Card for "Aug_DA_MultiWOZ_Restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/Aug_DA_MultiWOZ_Restaurants | [
"region:us"
]
| 2023-03-30T06:29:28+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "string"}, {"name": "speaker", "dtype": "int64"}, {"name": "generated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2728537, "num_examples": 7298}], "download_size": 1075591, "dataset_size": 2728537}} | 2023-03-30T06:29:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Aug_DA_MultiWOZ_Restaurants"
More Information needed | [
"# Dataset Card for \"Aug_DA_MultiWOZ_Restaurants\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Aug_DA_MultiWOZ_Restaurants\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Aug_DA_MultiWOZ_Restaurants\"\n\nMore Information needed"
]
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|
75e49504963ec14b7ebf5d4597ea2e097b06efaa | # Dataset Card for Swiss Leading Decisions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Swiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
Swiss Leading Decisions hepled in a text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents |
|------------|------------|----------------------|
| German | **de** | 14K |
| French | **fr** | 6K |
| Italian | **it** | 1K |
## Dataset Structure
### Data Fields
```
decision_id: (str) a unique identifier of the for the document
language: (int64) one of (0,1,2)
chamber_id: (int64) id to identfy chamber
file_id: (int64) id to identify file
date: (int64)
topic: (string)
year: (float64)
language: (string)
facts: (string) text section of the full text
facts_num_tokens_bert: (int64)
facts_num_tokens_spacy: (int64)
considerations: (string) text section of the full text
considerations_num_tokens_bert: (int64)
considerations_num_tokens_spacy: (int64)
rulings: (string) text section of the full text
rulings_num_tokens_bert: (int64)
rulings_num_tokens_spacy: (int64)
chamber (string):
court: (string)
canton: (string)
region: (string)
file_name: (string)
html_url: (string)
pdf_url: (string)
file_number: (string)
```
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
## Dataset Creation
### Curation Rationale
The dataset was created by Stern (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Stern5497](https://github.com/stern5497) for adding this dataset. | rcds/swiss_leading_decisions | [
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"language:it",
"language:fr",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
]
| 2023-03-30T07:38:35+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "it", "fr"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Swiss Leading Decisions"} | 2023-07-20T06:38:35+00:00 | [
"2306.09237"
]
| [
"de",
"it",
"fr"
]
| TAGS
#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-Italian #language-French #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us
| Dataset Card for Swiss Leading Decisions
========================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
Swiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
Swiss Leading Decisions hepled in a text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
Language: German, Subset: de, Number of Documents: 14K
Language: French, Subset: fr, Number of Documents: 6K
Language: Italian, Subset: it, Number of Documents: 1K
Dataset Structure
-----------------
### Data Fields
### Data Instances
### Data Fields
### Data Splits
Dataset Creation
----------------
### Curation Rationale
The dataset was created by Stern (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (URL).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (URL
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: URL
Please cite our ArXiv-Preprint
### Contributions
Thanks to @Stern5497 for adding this dataset.
| [
"### Dataset Summary\n\n\nSwiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nSwiss Leading Decisions hepled in a text classification task",
"### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 14K\nLanguage: French, Subset: fr, Number of Documents: 6K\nLanguage: Italian, Subset: it, Number of Documents: 1K\n\n\nDataset Structure\n-----------------",
"### Data Fields",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-Italian #language-French #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n",
"### Dataset Summary\n\n\nSwiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nSwiss Leading Decisions hepled in a text classification task",
"### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 14K\nLanguage: French, Subset: fr, Number of Documents: 6K\nLanguage: Italian, Subset: it, Number of Documents: 1K\n\n\nDataset Structure\n-----------------",
"### Data Fields",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-Italian #language-French #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n### Dataset Summary\n\n\nSwiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.### Supported Tasks and Leaderboards\n\n\nSwiss Leading Decisions hepled in a text classification task### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 14K\nLanguage: French, Subset: fr, Number of Documents: 6K\nLanguage: Italian, Subset: it, Number of Documents: 1K\n\n\nDataset Structure\n-----------------### Data Fields### Data Instances### Data Fields### Data Splits\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThe dataset was created by Stern (2023).### Source Data#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.### Annotations#### Annotation process#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL)."
]
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|
6b47376fa7f01cf2d220e4d3d2a7dd311c58caf6 | # Dataset Card for "stackoverflow_question_types"
## NOTE: the dataset is still currently under annotation
## Dataset Description
Recent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks.
However, users can ask a wide range of questions on stackoverflow; The "stackoverflow question types" is a dataset of manually annotated questions
posted on SO with an associated type. Following a previous [study](https://ieeexplore.ieee.org/document/6405249), each question was annotated with a type
capturing the main concern of the user who posted the question. The questions were annotated with the given types:
* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).
* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).
* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour.
* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.
* *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples)
* *Other*: a question related to another aspect of programming, or even non-related to programming.
### Remarks
For this dataset, we are mainly interested in questions related to *programming*.
For instance, for [this question](https://stackoverflow.com/questions/51142399/no-acceptable-c-compiler-found-in-path-installing-python-and-gcc),
the user is "trying to install Python-3.6.5 on a machine that does not have any package manager installed" and is facing issues.
Because it's not related to the concept of programming, we would classify it as "other" and not "debugging".
Moreover, we note the following conceptual distinctions between the different categories:
- Need to know: the user asks "is it possible to do x"
- How to do it: the user wants to do "x", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x".
- Debug: the user wants to do "x", and has a clear idea/solution "y" but it is not working, and is seeking a correction to "y".
- Seeking-different-solution: the user wants to do "x", and has found already a working solution "y", but is seeking an alternative "z".
Sometimes, it's hard to truly understand the users' true intentions;
the separating line between each category will be minor and might be subject to interpretation.
Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories).
However, this dataset contains mainly questions for which we could assign a clear single category to each question.
Currently, all questions annotated are a subset of the [stackoverflow_python](koutch/stackoverflow_python) dataset.
### Languages
The currently annotated questions concern posts with the *python* tag. The questions are written in *English*.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- question_id: the unique id of the post
- question_body: the (HTML) content of the question
- question_type: the assigned category/type/label
- "needtoknow"
- "howto",
- "debug",
- "seeking",
- "conceptual",
- "other"
### Data Splits
[More Information Needed]
## Dataset Creation
### Annotations
#### Annotation process
Previous research looked into mining natural language-code pairs from stackoverflow.
Two notable works yielded the [StaQC](https://arxiv.org/abs/1803.09371) and [ConaLA](https://arxiv.org/abs/1803.09371) datasets.
Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at [staqc](https://huggingface.co/datasets/koutch/staqc),
and [conala](https://huggingface.co/datasets/neulab/conala])). The questions were annotated as belonging to the "how to do it" category.
To ease the annotation procedure, we used the [argilla platform](https://docs.argilla.io/en/latest/index.html)
and multiple iterations of [few-shot training with a SetFit model](https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-setfit-zeroshot.html#%F0%9F%A6%BE-Train-a-few-shot-SetFit-model).
## Considerations for Using the Data
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed] | koutch/stackoverflow_question_types | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"code",
"arxiv:1803.09371",
"region:us"
]
| 2023-03-30T07:44:05+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "staqt", "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "question_body", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "question_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3433758, "num_examples": 3449}, {"name": "test", "num_bytes": 12055, "num_examples": 14}], "download_size": 0, "dataset_size": 3445813}, "tags": ["code"]} | 2023-04-10T13:45:23+00:00 | [
"1803.09371"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-cc #code #arxiv-1803.09371 #region-us
| # Dataset Card for "stackoverflow_question_types"
## NOTE: the dataset is still currently under annotation
## Dataset Description
Recent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks.
However, users can ask a wide range of questions on stackoverflow; The "stackoverflow question types" is a dataset of manually annotated questions
posted on SO with an associated type. Following a previous study, each question was annotated with a type
capturing the main concern of the user who posted the question. The questions were annotated with the given types:
* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).
* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).
* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour.
* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.
* *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples)
* *Other*: a question related to another aspect of programming, or even non-related to programming.
### Remarks
For this dataset, we are mainly interested in questions related to *programming*.
For instance, for this question,
the user is "trying to install Python-3.6.5 on a machine that does not have any package manager installed" and is facing issues.
Because it's not related to the concept of programming, we would classify it as "other" and not "debugging".
Moreover, we note the following conceptual distinctions between the different categories:
- Need to know: the user asks "is it possible to do x"
- How to do it: the user wants to do "x", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x".
- Debug: the user wants to do "x", and has a clear idea/solution "y" but it is not working, and is seeking a correction to "y".
- Seeking-different-solution: the user wants to do "x", and has found already a working solution "y", but is seeking an alternative "z".
Sometimes, it's hard to truly understand the users' true intentions;
the separating line between each category will be minor and might be subject to interpretation.
Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories).
However, this dataset contains mainly questions for which we could assign a clear single category to each question.
Currently, all questions annotated are a subset of the stackoverflow_python dataset.
### Languages
The currently annotated questions concern posts with the *python* tag. The questions are written in *English*.
## Dataset Structure
### Data Instances
### Data Fields
- question_id: the unique id of the post
- question_body: the (HTML) content of the question
- question_type: the assigned category/type/label
- "needtoknow"
- "howto",
- "debug",
- "seeking",
- "conceptual",
- "other"
### Data Splits
## Dataset Creation
### Annotations
#### Annotation process
Previous research looked into mining natural language-code pairs from stackoverflow.
Two notable works yielded the StaQC and ConaLA datasets.
Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at staqc,
and conala). The questions were annotated as belonging to the "how to do it" category.
To ease the annotation procedure, we used the argilla platform
and multiple iterations of few-shot training with a SetFit model.
## Considerations for Using the Data
### Discussion of Biases
### Other Known Limitations
| [
"# Dataset Card for \"stackoverflow_question_types\"",
"## NOTE: the dataset is still currently under annotation",
"## Dataset Description\n\nRecent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. \nHowever, users can ask a wide range of questions on stackoverflow; The \"stackoverflow question types\" is a dataset of manually annotated questions\nposted on SO with an associated type. Following a previous study, each question was annotated with a type \ncapturing the main concern of the user who posted the question. The questions were annotated with the given types: \n\n* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).\n* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).\n* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour. \n* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.\n* *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples)\n* *Other*: a question related to another aspect of programming, or even non-related to programming.",
"### Remarks\n\nFor this dataset, we are mainly interested in questions related to *programming*. \nFor instance, for this question, \nthe user is \"trying to install Python-3.6.5 on a machine that does not have any package manager installed\" and is facing issues. \nBecause it's not related to the concept of programming, we would classify it as \"other\" and not \"debugging\".\n\nMoreover, we note the following conceptual distinctions between the different categories:\n\n- Need to know: the user asks \"is it possible to do x\"\n- How to do it: the user wants to do \"x\", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving \"x\".\n- Debug: the user wants to do \"x\", and has a clear idea/solution \"y\" but it is not working, and is seeking a correction to \"y\".\n- Seeking-different-solution: the user wants to do \"x\", and has found already a working solution \"y\", but is seeking an alternative \"z\". \n\nSometimes, it's hard to truly understand the users' true intentions; \nthe separating line between each category will be minor and might be subject to interpretation. \nNaturally, some questions may have multiple concerns (i.e. could correspond to multiple categories). \nHowever, this dataset contains mainly questions for which we could assign a clear single category to each question. \nCurrently, all questions annotated are a subset of the stackoverflow_python dataset.",
"### Languages\n\nThe currently annotated questions concern posts with the *python* tag. The questions are written in *English*.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- question_id: the unique id of the post\n- question_body: the (HTML) content of the question\n- question_type: the assigned category/type/label\n - \"needtoknow\"\n - \"howto\",\n - \"debug\",\n - \"seeking\",\n - \"conceptual\",\n - \"other\"",
"### Data Splits",
"## Dataset Creation",
"### Annotations",
"#### Annotation process\n\nPrevious research looked into mining natural language-code pairs from stackoverflow. \nTwo notable works yielded the StaQC and ConaLA datasets.\nParts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at staqc, \nand conala). The questions were annotated as belonging to the \"how to do it\" category.\n\nTo ease the annotation procedure, we used the argilla platform \nand multiple iterations of few-shot training with a SetFit model.",
"## Considerations for Using the Data",
"### Discussion of Biases",
"### Other Known Limitations"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-cc #code #arxiv-1803.09371 #region-us \n",
"# Dataset Card for \"stackoverflow_question_types\"",
"## NOTE: the dataset is still currently under annotation",
"## Dataset Description\n\nRecent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. \nHowever, users can ask a wide range of questions on stackoverflow; The \"stackoverflow question types\" is a dataset of manually annotated questions\nposted on SO with an associated type. Following a previous study, each question was annotated with a type \ncapturing the main concern of the user who posted the question. The questions were annotated with the given types: \n\n* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).\n* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).\n* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour. \n* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.\n* *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples)\n* *Other*: a question related to another aspect of programming, or even non-related to programming.",
"### Remarks\n\nFor this dataset, we are mainly interested in questions related to *programming*. \nFor instance, for this question, \nthe user is \"trying to install Python-3.6.5 on a machine that does not have any package manager installed\" and is facing issues. \nBecause it's not related to the concept of programming, we would classify it as \"other\" and not \"debugging\".\n\nMoreover, we note the following conceptual distinctions between the different categories:\n\n- Need to know: the user asks \"is it possible to do x\"\n- How to do it: the user wants to do \"x\", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving \"x\".\n- Debug: the user wants to do \"x\", and has a clear idea/solution \"y\" but it is not working, and is seeking a correction to \"y\".\n- Seeking-different-solution: the user wants to do \"x\", and has found already a working solution \"y\", but is seeking an alternative \"z\". \n\nSometimes, it's hard to truly understand the users' true intentions; \nthe separating line between each category will be minor and might be subject to interpretation. \nNaturally, some questions may have multiple concerns (i.e. could correspond to multiple categories). \nHowever, this dataset contains mainly questions for which we could assign a clear single category to each question. \nCurrently, all questions annotated are a subset of the stackoverflow_python dataset.",
"### Languages\n\nThe currently annotated questions concern posts with the *python* tag. The questions are written in *English*.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- question_id: the unique id of the post\n- question_body: the (HTML) content of the question\n- question_type: the assigned category/type/label\n - \"needtoknow\"\n - \"howto\",\n - \"debug\",\n - \"seeking\",\n - \"conceptual\",\n - \"other\"",
"### Data Splits",
"## Dataset Creation",
"### Annotations",
"#### Annotation process\n\nPrevious research looked into mining natural language-code pairs from stackoverflow. \nTwo notable works yielded the StaQC and ConaLA datasets.\nParts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at staqc, \nand conala). The questions were annotated as belonging to the \"how to do it\" category.\n\nTo ease the annotation procedure, we used the argilla platform \nand multiple iterations of few-shot training with a SetFit model.",
"## Considerations for Using the Data",
"### Discussion of Biases",
"### Other Known Limitations"
]
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"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-cc #code #arxiv-1803.09371 #region-us \n# Dataset Card for \"stackoverflow_question_types\"## NOTE: the dataset is still currently under annotation## Dataset Description\n\nRecent research has taken a look into leveraging data available from StackOverflow (SO) to train large language models for programming-related tasks. \nHowever, users can ask a wide range of questions on stackoverflow; The \"stackoverflow question types\" is a dataset of manually annotated questions\nposted on SO with an associated type. Following a previous study, each question was annotated with a type \ncapturing the main concern of the user who posted the question. The questions were annotated with the given types: \n\n* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).\n* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).\n* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour. \n* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.\n* *Conceptual*: The question seeks to understand some aspects of programming (with or without using code examples)\n* *Other*: a question related to another aspect of programming, or even non-related to programming."
]
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|
ec1591c4dd8fd3f93a2060a5a5a2a181c8d4e190 |
# Dataset Card for coins-1apki
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/coins-1apki
- **Point of Contact:** [email protected]
### Dataset Summary
coins-1apki
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/coins-1apki
### Citation Information
```
@misc{ coins-1apki,
title = { coins 1apki Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/coins-1apki } },
url = { https://universe.roboflow.com/object-detection/coins-1apki },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/coins-1apki | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:08:48+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "coins-1apki", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "coins", "1": "coin", "2": "nail", "3": "nut", "4": "screw"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for coins-1apki
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
coins-1apki
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for coins-1apki\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncoins-1apki",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for coins-1apki\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncoins-1apki",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for coins-1apki\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncoins-1apki### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a95f6f82ac0402eaf7303f5b20a029b2754185ce |
# Dataset Card for circuit-elements
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/circuit-elements
- **Point of Contact:** [email protected]
### Dataset Summary
circuit-elements
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/circuit-elements
### Citation Information
```
@misc{ circuit-elements,
title = { circuit elements Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/circuit-elements } },
url = { https://universe.roboflow.com/object-detection/circuit-elements },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/circuit-elements | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:08:51+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "circuit-elements", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "circuit", "1": "Button", "2": "Buzzer", "3": "Capacitor", "4": "Capacitor Jumper", "5": "Capacitor Network", "6": "Clock", "7": "Connector", "8": "Diode", "9": "EM", "10": "Electrolytic Capacitor", "11": "Electrolytic capacitor", "12": "Ferrite Bead", "13": "Flex Cable", "14": "Fuse", "15": "IC", "16": "Inductor", "17": "Jumper", "18": "Led", "19": "Pads", "20": "Pins", "21": "Potentiometer", "22": "RP", "23": "Resistor", "24": "Resistor Jumper", "25": "Resistor Network", "26": "Switch", "27": "Test Point", "28": "Transducer", "29": "Transformer", "30": "Transistor", "31": "Unknown Unlabeled"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:09:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for circuit-elements
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
circuit-elements
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for circuit-elements\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncircuit-elements",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for circuit-elements\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncircuit-elements",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for circuit-elements\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncircuit-elements### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1bbdaa7077c2c49d8c30718d676738c575daac44 |
# Dataset Card for soda-bottles
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/soda-bottles
- **Point of Contact:** [email protected]
### Dataset Summary
soda-bottles
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/soda-bottles
### Citation Information
```
@misc{ soda-bottles,
title = { soda bottles Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/soda-bottles } },
url = { https://universe.roboflow.com/object-detection/soda-bottles },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/soda-bottles | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:09:00+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "soda-bottles", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "soda-bottles", "1": "coca-cola", "2": "fanta", "3": "sprite"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:00+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for soda-bottles
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
soda-bottles
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for soda-bottles\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsoda-bottles",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for soda-bottles\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsoda-bottles",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for soda-bottles\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsoda-bottles### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
77db0a73fc986d9619382d424c1aac6ece7d52bb |
# Dataset Card for apex-videogame
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/apex-videogame
- **Point of Contact:** [email protected]
### Dataset Summary
apex-videogame
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/apex-videogame
### Citation Information
```
@misc{ apex-videogame,
title = { apex videogame Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/apex-videogame } },
url = { https://universe.roboflow.com/object-detection/apex-videogame },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/apex-videogame | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:09:02+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "apex-videogame", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "apex-game", "1": "avatar", "2": "object"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:05+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for apex-videogame
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
apex-videogame
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for apex-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\napex-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for apex-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\napex-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
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233,
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5,
11,
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]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for apex-videogame\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\napex-videogame### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
4b88e2c64beca7f6cb67ff8e5d96aa22b9bfe33c |
# Dataset Card for radio-signal
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/radio-signal
- **Point of Contact:** [email protected]
### Dataset Summary
radio-signal
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/radio-signal
### Citation Information
```
@misc{ radio-signal,
title = { radio signal Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/radio-signal } },
url = { https://universe.roboflow.com/object-detection/radio-signal },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/radio-signal | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:09:26+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "radio-signal", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "radio-signal", "1": "stray", "2": "target"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:00+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for radio-signal
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
radio-signal
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for radio-signal\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nradio-signal",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for radio-signal\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nradio-signal",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for radio-signal\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nradio-signal### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
120407039f4ea390a3a8e9bd310300b656c611ae |
# Dataset Card for brain-tumor-m2pbp
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/brain-tumor-m2pbp
- **Point of Contact:** [email protected]
### Dataset Summary
brain-tumor-m2pbp
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/brain-tumor-m2pbp
### Citation Information
```
@misc{ brain-tumor-m2pbp,
title = { brain tumor m2pbp Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/brain-tumor-m2pbp } },
url = { https://universe.roboflow.com/object-detection/brain-tumor-m2pbp },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/brain-tumor-m2pbp | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:00+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "brain-tumor-m2pbp", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "brain-tumor", "1": "label0", "2": "label1", "3": "label2"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:06+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for brain-tumor-m2pbp
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
brain-tumor-m2pbp
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for brain-tumor-m2pbp\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbrain-tumor-m2pbp",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for brain-tumor-m2pbp\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbrain-tumor-m2pbp",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for brain-tumor-m2pbp\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nbrain-tumor-m2pbp### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a28d425f82ba36f702572e0a6f2116494e13b81d |
# Dataset Card for liver-disease
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/liver-disease
- **Point of Contact:** [email protected]
### Dataset Summary
liver-disease
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/liver-disease
### Citation Information
```
@misc{ liver-disease,
title = { liver disease Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/liver-disease } },
url = { https://universe.roboflow.com/object-detection/liver-disease },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/liver-disease | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:00+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "liver-disease", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "diseases", "1": "ballooning", "2": "fibrosis", "3": "inflammation", "4": "steatosis"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for liver-disease
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
liver-disease
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for liver-disease\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nliver-disease",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for liver-disease\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nliver-disease",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
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5,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for liver-disease\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nliver-disease### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
410dedecfb43ff44e7f52f10864198fe2d343d49 |
# Dataset Card for lettuce-pallets
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/lettuce-pallets
- **Point of Contact:** [email protected]
### Dataset Summary
lettuce-pallets
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/lettuce-pallets
### Citation Information
```
@misc{ lettuce-pallets,
title = { lettuce pallets Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/lettuce-pallets } },
url = { https://universe.roboflow.com/object-detection/lettuce-pallets },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/lettuce-pallets | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:05+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "lettuce-pallets", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "lettuce", "1": "Ready", "2": "empty_pod", "3": "germination", "4": "pod", "5": "young"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:45+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for lettuce-pallets
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
lettuce-pallets
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for lettuce-pallets\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nlettuce-pallets",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for lettuce-pallets\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nlettuce-pallets",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for lettuce-pallets\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nlettuce-pallets### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
6e10c727db27d24f42b075f41c3c2928f339d844 |
# Dataset Card for solar-panels-taxvb
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/solar-panels-taxvb
- **Point of Contact:** [email protected]
### Dataset Summary
solar-panels-taxvb
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/solar-panels-taxvb
### Citation Information
```
@misc{ solar-panels-taxvb,
title = { solar panels taxvb Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/solar-panels-taxvb } },
url = { https://universe.roboflow.com/object-detection/solar-panels-taxvb },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/solar-panels-taxvb | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:18+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "solar-panels-taxvb", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "solar-panels", "1": "Cell", "2": "Cell-Multi", "3": "No-Anomaly", "4": "Shadowing", "5": "Unclassified"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for solar-panels-taxvb
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
solar-panels-taxvb
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for solar-panels-taxvb\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsolar-panels-taxvb",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for solar-panels-taxvb\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsolar-panels-taxvb",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
26,
22,
14,
33,
5,
6,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for solar-panels-taxvb\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsolar-panels-taxvb### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
f599e8469b24e0474723fed566bca04c59c477f0 |
# Dataset Card for pills-sxdht
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/pills-sxdht
- **Point of Contact:** [email protected]
### Dataset Summary
pills-sxdht
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/pills-sxdht
### Citation Information
```
@misc{ pills-sxdht,
title = { pills sxdht Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/pills-sxdht } },
url = { https://universe.roboflow.com/object-detection/pills-sxdht },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/pills-sxdht | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:37+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "pills-sxdht", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "pills", "1": "Cipro 500", "2": "Ibuphil 600 mg", "3": "Ibuphil Cold 400-60", "4": "Xyzall 5mg", "5": "blue", "6": "pink", "7": "red", "8": "white"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:10:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for pills-sxdht
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
pills-sxdht
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for pills-sxdht\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npills-sxdht",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for pills-sxdht\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npills-sxdht",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for pills-sxdht\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npills-sxdht### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
264246eb60857e6908588032b66464a8ca2a9c59 |
# Dataset Card for trail-camera
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/trail-camera
- **Point of Contact:** [email protected]
### Dataset Summary
trail-camera
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/trail-camera
### Citation Information
```
@misc{ trail-camera,
title = { trail camera Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/trail-camera } },
url = { https://universe.roboflow.com/object-detection/trail-camera },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/trail-camera | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:45+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "trail-camera", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "game", "1": "Deer", "2": "Hog"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for trail-camera
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
trail-camera
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for trail-camera\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntrail-camera",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for trail-camera\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntrail-camera",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for trail-camera\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ntrail-camera### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
84d6a2506ce7524e3315d8e399e21642b187101d |
# Dataset Card for flir-camera-objects
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/flir-camera-objects
- **Point of Contact:** [email protected]
### Dataset Summary
flir-camera-objects
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/flir-camera-objects
### Citation Information
```
@misc{ flir-camera-objects,
title = { flir camera objects Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/flir-camera-objects } },
url = { https://universe.roboflow.com/object-detection/flir-camera-objects },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/flir-camera-objects | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:10:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "flir-camera-objects", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "flir-camera-objects", "1": "bicycle", "2": "car", "3": "dog", "4": "person"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:13:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for flir-camera-objects
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
flir-camera-objects
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for flir-camera-objects\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nflir-camera-objects",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for flir-camera-objects\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nflir-camera-objects",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
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6,
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233,
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5,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for flir-camera-objects\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nflir-camera-objects### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
ee4c79f9d376e0fc7ceb6c6bd34f80b7f00c9725 |
# Dataset Card for soccer-players-5fuqs
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/soccer-players-5fuqs
- **Point of Contact:** [email protected]
### Dataset Summary
soccer-players-5fuqs
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/soccer-players-5fuqs
### Citation Information
```
@misc{ soccer-players-5fuqs,
title = { soccer players 5fuqs Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/soccer-players-5fuqs } },
url = { https://universe.roboflow.com/object-detection/soccer-players-5fuqs },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/soccer-players-5fuqs | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:06+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "soccer-players-5fuqs", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "soccer-players", "1": "football", "2": "player", "3": "referee"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:24+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for soccer-players-5fuqs
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
soccer-players-5fuqs
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for soccer-players-5fuqs\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsoccer-players-5fuqs",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for soccer-players-5fuqs\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsoccer-players-5fuqs",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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78,
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for soccer-players-5fuqs\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsoccer-players-5fuqs### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
ed151089e49a93907acd48173393f9a9c72b14d2 |
# Dataset Card for printed-circuit-board
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/printed-circuit-board
- **Point of Contact:** [email protected]
### Dataset Summary
printed-circuit-board
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/printed-circuit-board
### Citation Information
```
@misc{ printed-circuit-board,
title = { printed circuit board Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/printed-circuit-board } },
url = { https://universe.roboflow.com/object-detection/printed-circuit-board },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/printed-circuit-board | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "printed-circuit-board", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "printed-circuit-board", "1": "Button", "2": "Capacitor", "3": "Capacitor Jumper", "4": "Clock", "5": "Connector", "6": "Diode", "7": "EM", "8": "Electrolytic Capacitor", "9": "Ferrite Bead", "10": "IC", "11": "Inductor", "12": "Jumper", "13": "Led", "14": "Pads", "15": "Pins", "16": "Resistor", "17": "Resistor Jumper", "18": "Resistor Network", "19": "Switch", "20": "Test Point", "21": "Transistor", "22": "Unknown Unlabeled", "23": "iC"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:49+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for printed-circuit-board
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
printed-circuit-board
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for printed-circuit-board\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nprinted-circuit-board",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for printed-circuit-board\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nprinted-circuit-board",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for printed-circuit-board\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nprinted-circuit-board### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
cd92c83884cc7c0ba1a3fda584784f550321e16f |
# Dataset Card for construction-safety-gsnvb
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/construction-safety-gsnvb
- **Point of Contact:** [email protected]
### Dataset Summary
construction-safety-gsnvb
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/construction-safety-gsnvb
### Citation Information
```
@misc{ construction-safety-gsnvb,
title = { construction safety gsnvb Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/construction-safety-gsnvb } },
url = { https://universe.roboflow.com/object-detection/construction-safety-gsnvb },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/construction-safety-gsnvb | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:17+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "construction-safety-gsnvb", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "construction-safety", "1": "helmet", "2": "no-helmet", "3": "no-vest", "4": "person", "5": "vest"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:51+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for construction-safety-gsnvb
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
construction-safety-gsnvb
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for construction-safety-gsnvb\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nconstruction-safety-gsnvb",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for construction-safety-gsnvb\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nconstruction-safety-gsnvb",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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27,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for construction-safety-gsnvb\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nconstruction-safety-gsnvb### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c91f4148103355ee247fd0a9577f491940688d06 |
# Dataset Card for aerial-pool
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/aerial-pool
- **Point of Contact:** [email protected]
### Dataset Summary
aerial-pool
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/aerial-pool
### Citation Information
```
@misc{ aerial-pool,
title = { aerial pool Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-pool } },
url = { https://universe.roboflow.com/object-detection/aerial-pool },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/aerial-pool | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:24+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "aerial-pool", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "aerial-pool", "1": "black-hat", "2": "bodysurface", "3": "bodyunder", "4": "umpire", "5": "white-hat"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:11:53+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for aerial-pool
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
aerial-pool
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for aerial-pool\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-pool",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for aerial-pool\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-pool",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for aerial-pool\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\naerial-pool### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
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