Convolutional Neural Networks (CNNs) are pivotal in computer vision tasks. Below are key architectures and their characteristics:
1. Basic CNN Structure
A typical CNN consists of:
- Convolutional Layers (filters for feature extraction)
- Pooling Layers (reduces spatial dimensions)
- Fully Connected Layers (final classification)
- Activation Functions (e.g., ReLU)
2. Popular CNN Architectures
LeNet-5
- Use Case: Handwritten digit recognition
- Features: Simple design, foundational for CNNs
AlexNet
- Use Case: ImageNet classification
- Features: Introduced ReLU and dropout, improved performance
VGGNet
- Use Case: High accuracy in image classification
- Features: Uniform convolutional layers, 16–19 layers in total
ResNet
- Use Case: Deep networks with residual connections
- Features: Solves vanishing gradient problem, supports hundreds of layers
3. Applications of CNNs
- Image Classification: Explore more
- Object Detection: Learn about YOLO
- Image Segmentation: Understand U-Net
4. Key Considerations
- Depth vs. Width: Deeper networks capture more abstract features
- Optimization Techniques: Batch normalization, data augmentation
- Transfer Learning: Pretrained models like VGG16 for faster training
For deeper insights, check our AI Fundamentals Guide to understand the broader context of neural networks. 🚀