{"id":29022968,"url":"https://github.com/sabin74/image_classification_cifar10","last_synced_at":"2025-08-23T15:32:42.031Z","repository":{"id":299021473,"uuid":"1001847575","full_name":"sabin74/image_classification_CIFAR10","owner":"sabin74","description":"This project demonstrates how to build a deep learning image classifier using the CIFAR-10 dataset. 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Two approaches are implemented:\n1. A custom Convolutional Neural Network (CNN)\n2. A transfer learning model using VGG16\n\n---\n\n## 📊 Dataset Overview\n\n- **Dataset:** CIFAR-10\n- **Classes (10):** airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck\n- **Image Shape:** (32, 32, 3)\n- **Train/Test Split:** 50,000/10,000\n\n---\n\n## 🚀 Model Architectures\n\n### Custom CNN\n\n- 3 Convolutional blocks with MaxPooling\n- Batch Normalization and Dropout\n- Dense layers for final classification\n\n### Transfer Learning (VGG16)\n\n- `VGG16` with `include_top=False`\n- Input images resized to (224x224)\n- GlobalAveragePooling + Dense layers\n\n---\n\n## 🛠️ Key Techniques\n\n- CNN Architecture\n- Data Augmentation\n- Regularization (Dropout, L2)\n- Batch Normalization\n- Transfer Learning\n- Callbacks (EarlyStopping, ModelCheckpoint)\n- Evaluation (Confusion Matrix, Classification Report)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabin74%2Fimage_classification_cifar10","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsabin74%2Fimage_classification_cifar10","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabin74%2Fimage_classification_cifar10/lists"}