SlowFast
A PyTorch implementation of SlowFast based on ICCV 2019 paper SlowFast Networks for Video Recognition.
Requirements
conda install pytorch=1.9.1 torchvision cudatoolkit -c pytorch
pip install pytorchvideo
Dataset
kinetics-400 dataset is used in this repo, you could download these datasets from official websites. The data directory structure is shown as follows:
├──data
├── train
├── abseiling
├── _4YTwq0-73Y_000044_000054.mp4
└── ...
...
├── archery
same structure as abseiling
├── test
same structure as train
...
Usage
Train Model
python train.py --batch_size 16
optional arguments:
--data_root Datasets root path [default value is 'data']
--batch_size Number of videos in each mini-batch [default value is 8]
--epochs Number of epochs over the model to train [default value is 10]
--save_root Result saved root path [default value is 'result']
Test Model
python test.py --video_path data/test/beatboxing/5s_gFWie1Ys_000069_000079.mp4
optional arguments:
--model_path Model path [default value is 'result/slow_fast.pth']
--video_path Video path [default value is 'data/test/applauding/_V-dzjftmCQ_000023_000033.mp4']