leekwoon/oxfordpet_dataset_backup

Oxford-IIIT Pet Dataset Backup

Complete backup of the Oxford-IIIT Pet Dataset containing images of cats and dogs with breed annotations.

Download & Extract

# huggingface_hub 설치 (필요시)
pip install huggingface_hub

# 다운로드
huggingface-cli download leekwoon/oxfordpet_dataset_backup --repo-type dataset --local-dir ./oxfordpet_data

# 무결성 확인 (선택사항)
cd oxfordpet_data
md5sum -c checksums.md5

# 파일 합치기 및 압축 해제
cat data.tar.gz.part_* | tar -xzvf -

Dataset Information

Overview

The Oxford-IIIT Pet Dataset is a 37-category pet dataset with roughly 200 images for each class. The dataset contains images of cats and dogs belonging to 37 breeds, with breed annotations and segmentation masks. It's widely used for fine-grained classification, object detection, and segmentation tasks.

Statistics

• Total Images: ~7,390
• Training Images: ~3,680
• Validation Images: ~370
• Test Images: ~3,340
• Number of Classes: 37 (12 cat breeds + 25 dog breeds)
• Image Format: JPEG
• Average Resolution: Variable (approximately 400x500 pixels)

Directory Structure

oxfordpet/
├── train/                  # Training images
│   ├── Abyssinian/        # Cat breed
│   ├── Bengal/            # Cat breed
│   ├── Birman/            # Cat breed
│   ├── Bombay/            # Cat breed
│   ├── British_Shorthair/
│   ├── Egyptian_Mau/
│   ├── Maine_Coon/
│   ├── Persian/
│   ├── Ragdoll/
│   ├── Russian_Blue/
│   ├── Siamese/
│   ├── Sphynx/
│   ├── american_bulldog/  # Dog breed
│   ├── american_pit_bull_terrier/
│   ├── basset_hound/
│   ├── beagle/
│   ├── boxer/
│   ├── chihuahua/
│   ├── english_cocker_spaniel/
│   ├── english_setter/
│   ├── german_shorthaired/
│   ├── great_pyrenees/
│   ├── havanese/
│   ├── japanese_chin/
│   ├── keeshond/
│   ├── leonberger/
│   ├── miniature_pinscher/
│   ├── newfoundland/
│   ├── pomeranian/
│   ├── pug/
│   ├── saint_bernard/
│   ├── samoyed/
│   ├── scottish_terrier/
│   ├── shiba_inu/
│   ├── staffordshire_bull_terrier/
│   └── yorkshire_terrier/
├── val/                    # Validation images
│   └── [same structure as train]
└── test/                   # Test images
    └── [same structure as train]

Pet Breeds

Cat Breeds (12)

1. Abyssinian
2. Bengal
3. Birman
4. Bombay
5. British Shorthair
6. Egyptian Mau
7. Maine Coon
8. Persian
9. Ragdoll
10. Russian Blue
11. Siamese
12. Sphynx

Dog Breeds (25)

1. American Bulldog
2. American Pit Bull Terrier
3. Basset Hound
4. Beagle
5. Boxer
6. Chihuahua
7. English Cocker Spaniel
8. English Setter
9. German Shorthaired Pointer
10. Great Pyrenees
11. Havanese
12. Japanese Chin
13. Keeshond
14. Leonberger
15. Miniature Pinscher
16. Newfoundland
17. Pomeranian
18. Pug
19. Saint Bernard
20. Samoyed
21. Scottish Terrier
22. Shiba Inu
23. Staffordshire Bull Terrier
24. Wheaten Terrier
25. Yorkshire Terrier

Usage

Loading with PyTorch

from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                       std=[0.229, 0.224, 0.225]),
])

# Load training data
train_dataset = datasets.ImageFolder('oxfordpet/train', transform=transform)

# Load validation data
val_dataset = datasets.ImageFolder('oxfordpet/val', transform=transform)

# Load test data
test_dataset = datasets.ImageFolder('oxfordpet/test', transform=transform)

# Get class names
class_names = train_dataset.classes

Loading with TensorFlow

import tensorflow as tf

# Define preprocessing
def preprocess_image(image, label):
    image = tf.image.resize(image, [224, 224])
    image = tf.cast(image, tf.float32) / 255.0
    return image, label

# Load dataset
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'oxfordpet/train',
    image_size=(224, 224),
    batch_size=32
).map(preprocess_image)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'oxfordpet/val',
    image_size=(224, 224),
    batch_size=32
).map(preprocess_image)

Key Challenges

This dataset presents several interesting challenges:

• Fine-grained classification: Distinguishing between similar breeds
• Pose variation: Pets in various poses and orientations
• Scale variation: Different distances from camera
• Occlusion: Partial visibility of pets
• Background variation: Indoor and outdoor settings
• Intra-class variation: Same breed can look quite different

Dataset Features

• Breed classification: 37 different breeds of cats and dogs
• Species classification: Binary classification (cat vs dog)
• Segmentation masks: Available in original dataset (not included in this backup)
• Head bounding boxes: Available in original dataset (not included in this backup)

Important Notes

• Images have variable sizes and aspect ratios
• Some breeds have more images than others
• Images are real-world photos with natural backgrounds
• File naming convention: {breed}_{number}.jpg
• This is a popular benchmark for fine-grained classification

Citation

If you use this dataset, please cite the original paper:

@InProceedings{parkhi12a,
  author = {Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.},
  title = {Cats and Dogs},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2012}
}

License

The dataset is available for research purposes. The images were collected from the internet and each image may have its own copyright. For commercial use, please refer to the original dataset documentation.

Acknowledgments

This dataset was created by the Visual Geometry Group at the University of Oxford in collaboration with IIIT Hyderabad.