File size: 3,945 Bytes
b9af16b ff9b709 b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b 8cf0fda b9af16b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
dataset_info:
config_name: full
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': resistor
splits:
- name: train
num_bytes: 4166234.0
num_examples: 126
- name: validation
num_bytes: 91766.0
num_examples: 6
- name: test
num_bytes: 111846.0
num_examples: 3
download_size: 4342491
dataset_size: 4369846.0
configs:
- config_name: full
data_files:
- split: train
path: full/train-*
- split: validation
path: full/validation-*
- split: test
path: full/test-*
---
<div align="center">
<img width="640" alt="MithatGuner/resistordataset" src="https://huggingface.co/datasets/MithatGuner/resistordataset/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['resistor']
```
### Number of Images
```json
{'valid': 6, 'test': 3, 'train': 126}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("MithatGuner/resistordataset", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/harish-madhavan/resistordataset/dataset/1](https://universe.roboflow.com/harish-madhavan/resistordataset/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{
resistordataset_dataset,
title = { ResistorDataset Dataset },
type = { Open Source Dataset },
author = { Harish Madhavan },
howpublished = { \\url{ https://universe.roboflow.com/harish-madhavan/resistordataset } },
url = { https://universe.roboflow.com/harish-madhavan/resistordataset },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { sep },
note = { visited on 2024-07-16 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on December 7, 2022 at 8:42 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 135 images.
Resistor are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Stretch)
* Auto-contrast via adaptive equalization
The following augmentation was applied to create 3 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
The following transformations were applied to the bounding boxes of each image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
* Randomly crop between 0 and 20 percent of the bounding box
* Random brigthness adjustment of between -25 and +25 percent
* Salt and pepper noise was applied to 5 percent of pixels
|