|
--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': accordion |
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'1': airplanes |
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'2': anchor |
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'3': ant |
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'4': barrel |
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'5': bass |
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'6': beaver |
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'7': binocular |
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'8': bonsai |
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'9': brain |
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'10': brontosaurus |
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'11': buddha |
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'12': butterfly |
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'13': camera |
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'14': cannon |
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'15': car_side |
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'16': ceiling_fan |
|
'17': cellphone |
|
'18': chair |
|
'19': chandelier |
|
'20': cougar_body |
|
'21': cougar_face |
|
'22': crab |
|
'23': crayfish |
|
'24': crocodile |
|
'25': crocodile_head |
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'26': cup |
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'27': dalmatian |
|
'28': dollar_bill |
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'29': dolphin |
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'30': dragonfly |
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'31': electric_guitar |
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'32': elephant |
|
'33': emu |
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'34': euphonium |
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'35': ewer |
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'36': faces |
|
'37': faces_easy |
|
'38': ferry |
|
'39': flamingo |
|
'40': flamingo_head |
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'41': garfield |
|
'42': gerenuk |
|
'43': gramophone |
|
'44': grand_piano |
|
'45': hawksbill |
|
'46': headphone |
|
'47': hedgehog |
|
'48': helicopter |
|
'49': ibis |
|
'50': inline_skate |
|
'51': joshua_tree |
|
'52': kangaroo |
|
'53': ketch |
|
'54': lamp |
|
'55': laptop |
|
'56': leopards |
|
'57': llama |
|
'58': lobster |
|
'59': lotus |
|
'60': mandolin |
|
'61': mayfly |
|
'62': menorah |
|
'63': metronome |
|
'64': minaret |
|
'65': motorbikes |
|
'66': nautilus |
|
'67': octopus |
|
'68': okapi |
|
'69': pagoda |
|
'70': panda |
|
'71': pigeon |
|
'72': pizza |
|
'73': platypus |
|
'74': pyramid |
|
'75': revolver |
|
'76': rhino |
|
'77': rooster |
|
'78': saxophone |
|
'79': schooner |
|
'80': scissors |
|
'81': scorpion |
|
'82': sea_horse |
|
'83': snoopy |
|
'84': soccer_ball |
|
'85': stapler |
|
'86': starfish |
|
'87': stegosaurus |
|
'88': stop_sign |
|
'89': strawberry |
|
'90': sunflower |
|
'91': tick |
|
'92': trilobite |
|
'93': umbrella |
|
'94': watch |
|
'95': water_lilly |
|
'96': wheelchair |
|
'97': wild_cat |
|
'98': windsor_chair |
|
'99': wrench |
|
'100': yin_yang |
|
splits: |
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- name: train |
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num_bytes: 121007587.037 |
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num_examples: 8677 |
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download_size: 121217709 |
|
dataset_size: 121007587.037 |
|
configs: |
|
- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: unknown |
|
task_categories: |
|
- image-classification |
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size_categories: |
|
- 1K<n<10K |
|
--- |
|
# Dataset Card for Caltech 101 |
|
|
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This dataset contains images of objects from 101 distinct categories, with each category comprising approximately 40 to 800 images. The majority of categories include around 50 images each. The images were collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc’Aurelio Ranzato. Each image has an approximate resolution of 300 x 200 pixels. |
|
|
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### Dataset Sources |
|
|
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- **Website:** https://data.caltech.edu/records/mzrjq-6wc02 |
|
|
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## Use in FL |
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|
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In order to prepare the dataset for the FL settings, we recommend using [Flower Dataset](https://flower.ai/docs/datasets/) (flwr-datasets) for the dataset download and partitioning and [Flower](https://flower.ai/docs/framework/) (flwr) for conducting FL experiments. |
|
|
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To partition the dataset, do the following. |
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1. Install the package. |
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```bash |
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pip install flwr-datasets[vision] |
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``` |
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2. Use the HF Dataset under the hood in Flower Datasets. |
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```python |
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from flwr_datasets import FederatedDataset |
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from flwr_datasets.partitioner import IidPartitioner |
|
|
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fds = FederatedDataset( |
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dataset="flwrlabs/caltech101", |
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partitioners={"train": IidPartitioner(num_partitions=10)} |
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) |
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partition = fds.load_partition(partition_id=0) |
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``` |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
The first instance of the train split is presented below: |
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``` |
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{ |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=397x150>, |
|
'label': 1 |
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} |
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``` |
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### Data Split |
|
|
|
``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['image', 'label'], |
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num_rows: 8677 |
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}) |
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}) |
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``` |
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|
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## Implementation details |
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|
|
Note that in this implementation, the string labels are first transformed into lowercase and then sorted alphabetically before providing the integer mapping. This methodology can vary across implementations. |
|
|
|
## Citation |
|
|
|
When working with the Caltech-101 dataset, please cite the original paper. |
|
If you're using this dataset with Flower Datasets and Flower, cite Flower. |
|
|
|
**BibTeX:** |
|
|
|
Dataset Bibtex: |
|
|
|
``` |
|
@misc{li2022caltech, |
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title = {Caltech 101}, |
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author = {Li, Fei-Fei and Andreeto, Marco and Ranzato, Marc'Aurelio and Perona, Pietro}, |
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year = {2022}, |
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month = {Apr}, |
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publisher = {CaltechDATA}, |
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doi = {10.22002/D1.20086}, |
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abstract = {Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a MATLAB script to view the annotations, 'show_annotations.m'.} |
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} |
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```` |
|
|
|
Flower: |
|
|
|
``` |
|
@article{DBLP:journals/corr/abs-2007-14390, |
|
author = {Daniel J. Beutel and |
|
Taner Topal and |
|
Akhil Mathur and |
|
Xinchi Qiu and |
|
Titouan Parcollet and |
|
Nicholas D. Lane}, |
|
title = {Flower: {A} Friendly Federated Learning Research Framework}, |
|
journal = {CoRR}, |
|
volume = {abs/2007.14390}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2007.14390}, |
|
eprinttype = {arXiv}, |
|
eprint = {2007.14390}, |
|
timestamp = {Mon, 03 Aug 2020 14:32:13 +0200}, |
|
biburl = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib}, |
|
bibsource = {dblp computer science bibliography, https://dblp.org} |
|
} |
|
``` |
|
|
|
## Dataset Card Contact |
|
|
|
If you have any questions about the dataset preprocessing and preparation, please contact [Flower Labs](https://flower.ai/). |
