piergiaj/pytorch-i3d is ranked 18,970 among these 8 peers; damo-cv/TransReID is ranked 18,969.

I3D models trained on Kinetics Overview This repository contains trained models reported in the paper "Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset" by Joao Carreira and Andrew Zisserman. This code is based on Deepmind's Kinetics-I3D. Including PyTorch versions of their models. Note This code was written for PyTorch 0. Fine-tuning and Feature Extraction We provide code to extract I3D features and fine-tune I3D for charades. Our fine-tuned models on charades are also

available in the models director (in addition to Deepmind's trained models). The deepmind pre-trained models were converted to PyTorch and give identical results (flow_imagenet. These models were pretrained on imagenet and kinetics (see Kinetics-I3D for details). py contains the code to fine-tune I3D based on the details in the paper and obtained from the authors. Specifically, this version follows the settings to fine-tune on the Charades dataset based on the author's implementation that won the

Charades 2017 challenge. Our fine-tuned RGB and Flow I3D models are available in the model directory (rgb_charades. This relied on having the optical flow and RGB frames extracted and saved as images on dist. py contains our code to load video segments for training. py contains the code to load a pre-trained I3D model and extract the features and save the features as numpy arrays. py script loads an entire video to extract per-segment features.