Vision Transformers
LeVIT
Introduced by Graham et al. in LeViT: a Vision Transformer in ConvNet's Clothing for Faster InferenceLeVIT is a hybrid neural network for fast inference image classification. LeViT is a stack of transformer blocks, with pooling steps to reduce the resolution of the activation maps as in classical convolutional architectures. This replaces the uniform structure of a Transformer by a pyramid with pooling, similar to the LeNet architecture
Source: LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Anomaly Detection | 1 | 33.33% |
| General Classification | 1 | 33.33% |
| Image Classification | 1 | 33.33% |
1x1 Convolution
Batch Normalization
Convolution
Hard Swish
Layer Normalization
LeViT Attention Block
Multi-Head Attention
Scaled Dot-Product Attention
Softmax
