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--- |
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size_categories: |
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- n<1K |
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task_categories: |
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- image-classification |
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- image-segmentation |
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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': antelope |
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'1': badger |
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'2': bat |
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'3': bear |
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'4': bee |
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'5': beetle |
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'6': bison |
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'7': boar |
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'8': butterfly |
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'9': cat |
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'10': caterpillar |
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'11': chimpanzee |
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'12': cockroach |
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'13': cow |
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'14': coyote |
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'15': crab |
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'16': crow |
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'17': deer |
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'18': dog |
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'19': dolphin |
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'20': donkey |
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'21': dragonfly |
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'22': duck |
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'23': eagle |
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'24': elephant |
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'25': flamingo |
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'26': fly |
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'27': fox |
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'28': goat |
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'29': goldfish |
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'30': goose |
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'31': gorilla |
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'32': grasshopper |
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'33': hamster |
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'34': hare |
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'35': hedgehog |
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'36': hippopotamus |
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'37': hornbill |
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'38': horse |
|
'39': hummingbird |
|
'40': hyena |
|
'41': jellyfish |
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'42': kangaroo |
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'43': koala |
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'44': ladybugs |
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'45': leopard |
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'46': lion |
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'47': lizard |
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'48': lobster |
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'49': mosquito |
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'50': moth |
|
'51': mouse |
|
'52': octopus |
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'53': okapi |
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'54': orangutan |
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'55': otter |
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'56': owl |
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'57': ox |
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'58': oyster |
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'59': panda |
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'60': parrot |
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'61': pelecaniformes |
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'62': penguin |
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'63': pig |
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'64': pigeon |
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'65': porcupine |
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'66': possum |
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'67': raccoon |
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'68': rat |
|
'69': reindeer |
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'70': rhinoceros |
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'71': sandpiper |
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'72': seahorse |
|
'73': seal |
|
'74': shark |
|
'75': sheep |
|
'76': snake |
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'77': sparrow |
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'78': squid |
|
'79': squirrel |
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'80': starfish |
|
'81': swan |
|
'82': tiger |
|
'83': turkey |
|
'84': turtle |
|
'85': whale |
|
'86': wolf |
|
'87': wombat |
|
'88': woodpecker |
|
'89': zebra |
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splits: |
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- name: train |
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num_bytes: 520059675.84 |
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num_examples: 4320 |
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- name: test |
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num_bytes: 138887701.08 |
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num_examples: 1080 |
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download_size: 696270301 |
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dataset_size: 658947376.92 |
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configs: |
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- 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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- split: test |
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path: data/test-* |
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tags: |
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- animals |
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--- |
|
|
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# Dataset Card for Dataset Name |
|
|
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This dataset is a port of the ["Animal Image Dataset"](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) that you can find on Kaggle. |
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The dataset contains 60 pictures for 90 types of animals, with various image sizes. |
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|
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With respect to the original dataset, I created the train-test-split partitions (80%/20%) to make it compatible via HuggingFace `datasets`. |
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|
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**Note**. At the time of writing, by looking at the Croissant ML Metadata, the original license of the data is `sc:CreativeWork`. If you believe this dataset violates any license, please |
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open an issue in the discussion tab, so I can take action as soon as possible. |
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|
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## How to use this data |
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|
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```python |
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from datasets import load_dataset |
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|
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# for exploration |
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ds = load_dataset("lucabaggi/animal-wildlife", split="train") |
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|
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# for training |
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ds = load_dataset("lucabaggi/animal-wildlife") |
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``` |
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|
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## How the data was generated |
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|
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You can find the source code for the extraction pipeline [here](./extract.py). Note: partly generated with Claude3 and Codestral 😎😅 Please feel free to open an issue in the discussion sction if you wish to improve the code. |
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|
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``` |
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$ uv run --python=3.11 -- python -m extract --help |
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|
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usage: extract.py [-h] [--destination-dir DESTINATION_DIR] [--split-ratio SPLIT_RATIO] [--random-seed RANDOM_SEED] [--remove-zip] zip_file |
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|
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Reorganize dataset. |
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|
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positional arguments: |
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zip_file Path to the zip file. |
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|
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options: |
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-h, --help show this help message and exit |
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--destination-dir DESTINATION_DIR |
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Path to the destination directory. |
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--split-ratio SPLIT_RATIO |
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Ratio of data to be used for training. |
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--random-seed RANDOM_SEED |
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Random seed for reproducibility. |
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--remove-zip Whether to remove the source zip archive file after extraction. |
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``` |
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|
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Example usage: |
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|
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1. Download the data from Kaggle. You can use Kaggle Python SDK, but that might require an API key if you use it locally. |
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|
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2. Invoke the script: |
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|
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```bash |
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uv run --python=3.11 -- python -m extract -- archive.zip |
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``` |
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|
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This will explode the contents of the zip archive into a `data` directory, splitting the train and test dataset in a 80%/20% ratio. |
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|
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3. Upload to the hub: |
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|
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```python |
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from datasets import load_dataset |
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|
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ds = load_datset("imagefolder", data_dir="data") |
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ds.push_to_hub() |
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``` |
