Please see codes in build_data to prepare different datasets, you need to have kinetics at least to run the training. After that, please see codes in tf_model to train the model and evaluate it. Finally, check show_results.ipynb in notebook folder to see how the training progress can be checked and compared to our training trajectory.

Weights can be downloaded at this link.

Check function test_video_model in script tf_model/generate_resps_from_ckpt.py. The outputs will be stored in a dictionary, with keys like encode_x (x is from 1 to 10). Layer encode_1 is the output of the first pooling layer. The other layers are outputs from the following residual blocks (ResNet18 has 9 residual blocks in total). The output is in shape (batch_size, channels, temporal_dim, spatial_dim, spatial_dim).