Create README.md

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+license:
+- other
+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)