Vision_Transformer
Source:
AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE
Visual-Transformers-a-new-computer-vision-paradigm
Step 1: Split the image into fixed-size patches.
Step 2:Flatten the 2D image patches to 1D patch embedding and linearly embed them using a fully connected layer. Positional embeddings are added to the patch embeddings to retain positional information.
Step 3: A special classification token(CLS) is added to positional encoding, and the positional encoding is summed. The positional patch encoded vectors will be passed as an input to the Transformer Encoder.
Step 4: Transformer Encoder has alternating layers of multiheaded self-attention and MLP blocks. Layernorm (LN) is applied before the self-attention block, and the MLP block and the residual connections are applied after every block.
Step 5: A classification head is implemented using MLP with one hidden layer at pre-training time and a single linear layer for fine-tuning for image classification.
