some popular deep learning models and their primary use cases
1. ResNet (Residual Network)
- Purpose: Overcomes vanishing gradient issues in deep networks using residual connections.
- Applications: Image classification, object detection, feature extraction.
2. EfficientNet
- Purpose: Balances model size, computation cost, and accuracy using compound scaling.
- Applications: Image classification, transfer learning for vision tasks.
3. VGG (Visual Geometry Group)
- Purpose: Simplified convolutional network with a fixed architecture using convolution and pooling layers.
- Applications: Image classification, feature extraction.
4. Inception (GoogleNet)
- Purpose: Utilizes multi-scale convolutions within an “Inception module” to improve efficiency and accuracy.
- Applications: Image classification, object detection, scene recognition.
5. DenseNet (Dense Convolutional Network)
- Purpose: Improves feature reuse by connecting every layer to all subsequent layers.
- Applications: Image classification, medical image analysis.
6. MobileNet
- Purpose: Designed for mobile and embedded devices with lightweight and efficient architecture.
- Applications: Real-time image processing on mobile, object detection.
7. Xception
- Purpose: Employs depthwise separable convolutions for a more efficient network.
- Applications: Image classification, object detection.
8. Vision Transformer (ViT)
- Purpose: Applies transformer architectures to image classification by modeling global relationships.
- Applications: Image classification, visual-language tasks.
9. Swin Transformer
- Purpose: Hierarchical transformer for capturing both local and global features in images.
- Applications: Image classification, object detection, image segmentation.
10. NASNet (Neural Architecture Search Network)
- Purpose: Auto-generated architecture optimized for accuracy and computational efficiency.
- Applications: Image classification, object detection.
11. YOLO (You Only Look Once)
- Purpose: Real-time object detection by processing the entire image in a single pass.
- Applications: Object detection, real-time video analysis.
12. ConvNeXt
- Purpose: A modernized convolutional network with features inspired by transformers.
- Applications: Image classification, object detection.