Facebook video classification team  has recently released a new video classification model called TimeSformer, in a paper titled:  Is Space-Time Attention All You Need for Video Understanding? ,  which promises to outperform previous models while being more computationally efficient. In this blog post, we'll take a closer look at TimeSformer and how to train it on a custom video dataset using PyTorch. What is TimeSformer? TimeSformer is a neural network architecture for video classification. It is based on the Transformer architecture, which has been highly successful in natural language processing tasks. However, while the original Transformer was designed for sequential data, TimeSformer is designed for video data, which has both temporal and spatial dimensions. https://arxiv.org/pdf/2102.05095v4.pdf TimeSformer achieves this by applying the self-attention mechanism of the Transformer architecture across both time and space. This allows the model to learn dependencies between