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

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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.

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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).

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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.

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Usage Example

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}
}