ImageNet is a widely used large-scale image database, containing over 14 million labeled images across 21,000 object categories. It's a cornerstone for training and testing machine learning models, especially in the field of computer vision.

Key Features

  • 📈 Massive Scale: 1.2 million images per class for 1,000 core categories
  • 🧠 Hierarchical Structure: Organized by relationships between objects (e.g., "dog" → "Golden_Retriever")
  • 🎯 Standard Benchmark: Used in competitions like ILSVRC for evaluating CNN performance
  • 🌐 Multilingual Support: Includes annotations in English, Chinese, and other languages
ImageNet Dataset

Common Use Cases

  • 🤖 Pre-trained Models: Researchers often use ImageNet weights for transfer learning
  • 📊 Performance Metrics: Accuracy, precision, and recall are measured on ImageNet tasks
  • 🖼️ Image Classification: Ideal for training models to recognize objects in images

For deeper exploration of datasets, check our dataset collection page 📚
Learn more about CNN training with ImageNet ➡️