Welcome to the CIFAR-10 documentation! 📚 CIFAR-10 is a widely used dataset in machine learning research, consisting of 60,000 32x32彩色图像 across 10 classes. It's split into 50,000 training images and 10,000 test images, making it ideal for benchmarking classification algorithms.

Key Features

  • 10 Classes: Aircraft, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.
  • Image Size: 32x32 pixels (RGB format).
  • Use Cases: Image recognition, convolutional neural networks (CNNs), and computer vision tasks.

How to Use

  1. Download: Access the dataset via CIFAR-10 Download Page.
  2. Preprocessing: Normalize pixel values to [0,1] range and split into training/validation/test sets.
  3. Applications: Commonly used for training models in object detection, segmentation, and generative AI.

Example Images

CIFAR 10 Overview
Here are sample images from the dataset: - 🐾 **Cat**: [View Example](/Documentation/en/Datasets/CIFAR-10/Examples/Cat) - 🐶 **Dog**: [View Example](/Documentation/en/Datasets/CIFAR-10/Examples/Dog) - 🚗 **Truck**: [View Example](/Documentation/en/Datasets/CIFAR-10/Examples/Truck)

For deeper insights, explore our CIFAR-10 Technical Guide to learn about data augmentation and model evaluation metrics. 📈