README.md

---
annotations_creators:
- expert-generated
language:
- en
license: mit
multilinguality:
- monolingual
pretty_name: CIFAR-10
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
tags:
- computer-vision
- image-classification
- benchmark
- cifar
- object-detection
---

# CIFAR-10 - Object Recognition in Images

> Benchmark dataset for object classification.  
> 🖼️ 60,000 32x32 color images  
> 🏷️ 10 classes  
> 📁 Format: PNG, CSV  
> 📦 Files: 4  
> 🧪 Subset of the 80 million tiny images dataset

---

## Dataset Summary

**CIFAR-10** is a widely used computer vision dataset consisting of 60,000 32x32 color images in 10 mutually exclusive classes. It was created by **Alex Krizhevsky**, **Vinod Nair**, and **Geoffrey Hinton**. The dataset is a labeled subset of the 80 million tiny images dataset and is often used as a benchmark for image classification tasks.

This Hugging Face version mirrors the original Kaggle competition structure, including additional junk test images to discourage cheating.

---

## Dataset Structure

### Files Included

| File                 | Description                                   |
|----------------------|-----------------------------------------------|
| `train.7z`           | Training images in PNG format (50,000 images) |
| `test.7z`            | Test images in PNG format (300,000 images incl. junk) |
| `trainLabels.csv`    | Training image labels                         |
| `sampleSubmission.csv` | Sample format for submission predictions     |

### Label Classes

Each image is labeled with one of the following 10 classes:

- airplane  
- automobile  
- bird  
- cat  
- deer  
- dog  
- frog  
- horse  
- ship  
- truck

> **Note**: "automobile" includes sedans and SUVs; "truck" includes large trucks only (not pickups).

---

## Data Splits

| Split   | Number of Images |
|---------|------------------|
| Train   | 50,000           |
| Test    | 10,000 (scored) + 290,000 (junk) |

**Total:** 300,000 test image predictions are required, though only 10,000 are scored.

---

## Usage Example

```python
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.ToTensor()
])

trainset = CIFAR10(root='./data', train=True, download=True, transform=transform)
testset = CIFAR10(root='./data', train=False, download=True, transform=transform)
```

## Citation
If you use this dataset, please cite the original technical report:
```
@techreport{Krizhevsky2009LearningML,
  title={Learning Multiple Layers of Features from Tiny Images},
  author={Alex Krizhevsky},
  year={2009},
  institution={University of Toronto},
  url={https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf}
}
```