Datasets:
Tasks:
Image Classification
Modalities:
Image
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K<n<1M
License:
File size: 2,840 Bytes
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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}
}
``` |