## STAM - Pytorch Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in video classification. This corroborates the finding of TimeSformer. Attention is all we need. ## Install ```bash $ pip install stam-pytorch ``` ## Usage ```python import torch from stam_pytorch import STAM model = STAM( dim = 512, image_size = 256, # size of image patch_size = 32, # patch size num_frames = 5, # number of image frames, selected out of video space_depth = 12, # depth of vision transformer space_heads = 8, # heads of vision transformer space_mlp_dim = 2048, # feedforward hidden dimension of vision transformer time_depth = 6, # depth of time transformer (in paper, it was shallower, 6) time_heads = 8, # heads of time transformer time_mlp_dim = 2048, # feedforward hidden dimension of time transformer num_classes = 100, # number of output classes space_dim_head = 64, # space transformer head dimension time_dim_head = 64, # time transformer head dimension dropout = 0., # dropout emb_dropout = 0. # embedding dropout ) frames = torch.randn(2, 5, 3, 256, 256) # (batch x frames x channels x height x width) pred = model(frames) # (2, 100) ``` ## Citations ```bibtex @misc{sharir2021image, title = {An Image is Worth 16x16 Words, What is a Video Worth?}, author = {Gilad Sharir and Asaf Noy and Lihi Zelnik-Manor}, year = {2021}, eprint = {2103.13915}, archivePrefix = {arXiv}, primaryClass = {cs.CV} } ```