Video classification using Flash
Video classification is one of the most interesting yet challenging problems in DL. Simply speaking, it tries to classify an action in a video clip and recognize it (such as walking, bowling, or golfing):
Figure 4.1 – The Kinetics human action video dataset released by DeepMind is comprised of annotated ~10-second video clips sourced from YouTube
Training such a DL model is a challenging problem because of the sheer amount of compute power it takes to train the model, given the large size of video files compared to tabular or image data. Using a pre-trained model and architecture is a great way to start your experiments for video classification.
PyTorch Lightning Flash relies internally on the PyTorchVideo library for its backbone. PyTorchVideo caters to the ecosystem of video understanding. Lightning Flash makes it easy by creating the predefined and configurable hooks into the underlying framework. There are hooks...
