This repository contains the framework for UG2+ Track 2.1 Challenge: Fully Supervised Action Recognition in the Dark.

This code is based on PyTorch, you may need to install the following packages:

PyTorch >= 1.2 (tested on 1.2/1.4/1.5/1.6)
opencv-python (pip install)

Training:

python train_arid_t1.py --network <Network Name>

• There are a number of parameters that can be further tuned. We recommend a batch size of 8 per GPU. Here we provide an example where the 3D-ResNet (18 layers) network is used. This network is directly imported from torchvision. You may use any other networks by putting the network into the /network folder. Do note that it is recommended you run the network once within the /network folder to debug before you run training.

To generate the zipfile to be submitted, use the following commands:

cd predict
python predict_video.py

You may change the resulting zipfile name by changing the "--zip-file" configuration in the code, or simply by changing the configuration dynamically by

python predict_video.py --zip-file <YOUR PREFERRED ZIPFILE NAME>

• To view our paper, go to this arxiv link
• Our code base is adapted from Multi-Fiber Network for Video Recognition, we would like to thank the authors for providing the code base.