---
size_categories:
- n<1K
task_categories:
- image-classification
- image-segmentation
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': antelope
          '1': badger
          '2': bat
          '3': bear
          '4': bee
          '5': beetle
          '6': bison
          '7': boar
          '8': butterfly
          '9': cat
          '10': caterpillar
          '11': chimpanzee
          '12': cockroach
          '13': cow
          '14': coyote
          '15': crab
          '16': crow
          '17': deer
          '18': dog
          '19': dolphin
          '20': donkey
          '21': dragonfly
          '22': duck
          '23': eagle
          '24': elephant
          '25': flamingo
          '26': fly
          '27': fox
          '28': goat
          '29': goldfish
          '30': goose
          '31': gorilla
          '32': grasshopper
          '33': hamster
          '34': hare
          '35': hedgehog
          '36': hippopotamus
          '37': hornbill
          '38': horse
          '39': hummingbird
          '40': hyena
          '41': jellyfish
          '42': kangaroo
          '43': koala
          '44': ladybugs
          '45': leopard
          '46': lion
          '47': lizard
          '48': lobster
          '49': mosquito
          '50': moth
          '51': mouse
          '52': octopus
          '53': okapi
          '54': orangutan
          '55': otter
          '56': owl
          '57': ox
          '58': oyster
          '59': panda
          '60': parrot
          '61': pelecaniformes
          '62': penguin
          '63': pig
          '64': pigeon
          '65': porcupine
          '66': possum
          '67': raccoon
          '68': rat
          '69': reindeer
          '70': rhinoceros
          '71': sandpiper
          '72': seahorse
          '73': seal
          '74': shark
          '75': sheep
          '76': snake
          '77': sparrow
          '78': squid
          '79': squirrel
          '80': starfish
          '81': swan
          '82': tiger
          '83': turkey
          '84': turtle
          '85': whale
          '86': wolf
          '87': wombat
          '88': woodpecker
          '89': zebra
  splits:
  - name: train
    num_bytes: 520059675.84
    num_examples: 4320
  - name: test
    num_bytes: 138887701.08
    num_examples: 1080
  download_size: 696270301
  dataset_size: 658947376.92
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- animals
---

# Dataset Card for Dataset Name

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.
The dataset contains 60 pictures for 90 types of animals, with various image sizes.

With respect to the original dataset, I created the train-test-split partitions (80%/20%) to make it compatible via HuggingFace `datasets`.

**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 
open an issue in the discussion tab, so I can take action as soon as possible.

## How to use this data

```python
from datasets import load_dataset

# for exploration
ds = load_dataset("lucabaggi/animal-wildlife", split="train")

# for training
ds = load_dataset("lucabaggi/animal-wildlife")
```

## How the data was generated

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.

```
$ uv run --python=3.11 -- python -m extract --help

usage: extract.py [-h] [--destination-dir DESTINATION_DIR] [--split-ratio SPLIT_RATIO] [--random-seed RANDOM_SEED] [--remove-zip] zip_file

Reorganize dataset.

positional arguments:
  zip_file              Path to the zip file.

options:
  -h, --help            show this help message and exit
  --destination-dir DESTINATION_DIR
                        Path to the destination directory.
  --split-ratio SPLIT_RATIO
                        Ratio of data to be used for training.
  --random-seed RANDOM_SEED
                        Random seed for reproducibility.
  --remove-zip          Whether to remove the source zip archive file after extraction.
```

Example usage:

1. Download the data from Kaggle. You can use Kaggle Python SDK, but that might require an API key if you use it locally.

2. Invoke the script:

```bash
uv run --python=3.11 -- python -m extract -- archive.zip
```

This will explode the contents of the zip archive into a `data` directory, splitting the train and test dataset in a 80%/20% ratio.

3. Upload to the hub:

```python
from datasets import load_dataset

ds = load_datset("imagefolder", data_dir="data")
ds.push_to_hub()
```