Base (Reference) Framework for UG2+ Track 2.1 Challenge: Fully Supervised Action Recognition in the Dark

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

Prerequisites

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

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.

Testing

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>

Other Information

• For more about the rules, regulations about this competition, do visit our site here
• 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.
• You may contact me through [email protected]

ARID Classes

Class ID, Class
0         Drink
1         Jump
2         Pick
3         Pour
4         Push
5         Run
6         Sit
7         Stand
8         Turn
9         Walk
10        Wave

Data setup

cd ${ARID_Dataset}
gdown 10sitw9Mi9Gv1jMfyMwbv78EZSpW_lKEx

ln -sf ${ARID_Dataset}/clips_v1.5 ./dataset/ARID/raw/train_data
ln -sf ${ARID_Dataset}/clips_v1.5 ./dataset/ARID/raw/test_data


PRETRAINED_MODELS=/home/srinitca/vlr/project/pretrained
mkdir ${PRETRAINED_MODELS}
cd ${PRETRAINED_MODELS}

gdown 1uwW8iJyKkO4dnVZoV-wgN_1ko4f36nOk
gdown 1_PhrhMD90i_xns0kGJBLdUC9lQfXnhXX
gdown 1GKUfBqZ_L2nMiLT5z2l9Zo6m8eCfymOi
gdown 1eSdDlc1E3KIff88nxPoy92wg8agEjJjT
gdown 1btoVm82Jk61bIdLvz-lSVXcyzOUBcUdH
gdown 1eUL6fJ313xnbjMQNDxac7-OVec_hC2Td

# Back to project folder
cd -

ln -sf ${PRETRAINED_MODELS}/ ./network/pretrained