yangyunshi/imagenet-50-subset

---
dataset_info:
dataset_name: imagenet-50-subset
dataset_size: 50 classes, 50000 images
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
---

# ImageNet-50 Subset

This dataset contains the first 50 classes from ImageNet-1K with up to 1,000 images per class (where available).

## Dataset Statistics

- **Total Classes**: 50
- **Total Images**: 50000
- **Train/Val Split**: 90%/10%
- **Max Images per Class**: 1000

## Dataset Structure

```
imagenet-50-subset/
├── train/
│ ├── n01440764/ # tench
│ │ ├── n01440764_1234.JPEG
│ │ └── ...
│ ├── n01443537/ # goldfish
│ └── ...
├── val/
│ ├── n01440764/
│ ├── n01443537/
│ └── ...
├── metadata.json
├── wnid_to_class.txt
└── README.md
```

## Classes Included

| WordNet ID | Class Name | Train Images | Val Images | Total |
|------------|------------|--------------|------------|-------|
| n01440764 | tench | 900 | 100 | 1000 |
| n01443537 | goldfish | 900 | 100 | 1000 |
| n01484850 | great white shark | 900 | 100 | 1000 |
| n01491361 | tiger shark | 900 | 100 | 1000 |
| n01494475 | hammerhead | 900 | 100 | 1000 |
| n01496331 | electric ray | 900 | 100 | 1000 |
| n01498041 | stingray | 900 | 100 | 1000 |
| n01514668 | cock | 900 | 100 | 1000 |
| n01514859 | hen | 900 | 100 | 1000 |
| n01518878 | ostrich | 900 | 100 | 1000 |
| n01530575 | brambling | 900 | 100 | 1000 |
| n01531178 | goldfinch | 900 | 100 | 1000 |
| n01532829 | house finch | 900 | 100 | 1000 |
| n01534433 | junco | 900 | 100 | 1000 |
| n01537544 | indigo bunting | 900 | 100 | 1000 |
| n01558993 | robin | 900 | 100 | 1000 |
| n01560419 | bulbul | 900 | 100 | 1000 |
| n01580077 | jay | 900 | 100 | 1000 |
| n01582220 | magpie | 900 | 100 | 1000 |
| n01592084 | chickadee | 900 | 100 | 1000 |
| n01601694 | water ouzel | 900 | 100 | 1000 |
| n01608432 | kite | 900 | 100 | 1000 |
| n01614925 | bald eagle | 900 | 100 | 1000 |
| n01616318 | vulture | 900 | 100 | 1000 |
| n01622779 | great grey owl | 900 | 100 | 1000 |
| n01629819 | European fire salamander | 900 | 100 | 1000 |
| n01630670 | common newt | 900 | 100 | 1000 |
| n01631663 | eft | 900 | 100 | 1000 |
| n01632458 | spotted salamander | 900 | 100 | 1000 |
| n01632777 | axolotl | 900 | 100 | 1000 |
| n01641577 | bullfrog | 900 | 100 | 1000 |
| n01644373 | tree frog | 900 | 100 | 1000 |
| n01644900 | tailed frog | 900 | 100 | 1000 |
| n01664065 | loggerhead | 900 | 100 | 1000 |
| n01665541 | leatherback turtle | 900 | 100 | 1000 |
| n01667114 | mud turtle | 900 | 100 | 1000 |
| n01667778 | terrapin | 900 | 100 | 1000 |
| n01669191 | box turtle | 900 | 100 | 1000 |
| n01675722 | banded gecko | 900 | 100 | 1000 |
| n01677366 | common iguana | 900 | 100 | 1000 |
| n01682714 | American chameleon | 900 | 100 | 1000 |
| n01685808 | whiptail | 900 | 100 | 1000 |
| n01687978 | agama | 900 | 100 | 1000 |
| n01688243 | frilled lizard | 900 | 100 | 1000 |
| n01689811 | alligator lizard | 900 | 100 | 1000 |
| n01692333 | Gila monster | 900 | 100 | 1000 |
| n01693334 | green lizard | 900 | 100 | 1000 |
| n01694178 | African chameleon | 900 | 100 | 1000 |
| n01695060 | Komodo dragon | 900 | 100 | 1000 |
| n01697457 | African crocodile | 900 | 100 | 1000 |

## Usage with Hugging Face Datasets

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/imagenet-50-subset")

# Access train and validation splits
train_dataset = dataset['train']
val_dataset = dataset['validation']

# Example: Load and display an image
from PIL import Image
import matplotlib.pyplot as plt

sample = train_dataset[0]
image = Image.open(sample['image'])
label = sample['label']
plt.imshow(image)
plt.title(f"Class: {label}")
plt.show()
```

## Usage with PyTorch

```python
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define transforms
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])

# Load datasets
train_dataset = datasets.ImageFolder('./imagenet-50-subset/train', transform=transform)
val_dataset = datasets.ImageFolder('./imagenet-50-subset/val', transform=transform)

# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
```

## License

This subset inherits the ImageNet license. Please ensure you have the right to use ImageNet data.
The original ImageNet dataset is available at http://www.image-net.org/

## Citation

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

```bibtex
@article{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
journal={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={IEEE}
}
```