Transfer learning architecture and its features
| Model | Architecture | Features with ImageNet weights | Applications |
|---|---|---|---|
| VGG-16 | 16 weight layers (13 convolutional + 3 fully connected). | Robust feature extraction for classification tasks. | Transfer learning, object detection. |
| Small 3 × 3 convolutional kernels with 2 × 2 max-pooling layers. | Pretrained on ImageNet, provides generalizable features. | ||
| Requires significant memory due to its size. | |||
| Inception v3 | Modular architecture with inception blocks (1 × 1, 3 × 3, 5 × 5 convolutions). | Highly efficient and accurate for hierarchical feature extraction. | Image classification, segmentation, and image captioning. |
| Uses auxiliary classifiers to combat vanishing gradients. | Pretrained weights reduce the need for large datasets. | ||
| Dimensionality reduction within inception modules. | Optimized for efficiency without sacrificing performance. | ||
| EfficientNet B0 | Compound scaling balances network depth, width, and resolution. | High accuracy with minimal resources when pre-trained on ImageNet. | Edge computing, facial recognition, and anomaly detection. |
| Utilizes MBConv (mobile inverted bottleneck blocks) and squeeze-and-excitation layers. | Scales effectively to larger EfficientNet variants for higher accuracy. | ||
| Lightweight and resource-efficient, ideal for deployment on low-power devices. |