1. Download the dataset data.zip at (https://mega.nz/#!O6wXlSTS!wcEoDT4Ctq5HRq_hV-aWeVF1_JB3cacQBQqOLjCIbc8) or (https://zenodo.org/record/3625992#.Xiv9jGhKhPY). 
2. Unzip the data.zip file to the current folder. There are three datasets in the ./data folder, i.e. ./data/breakfast, ./data/50salads, ./data/gtea
3. Download the pre-trained models at (https://pan.baidu.com/s/1zf-d-7eYqK-IxroBKTxDfg). There are pretrained models for three datasets, i.e. ./models/50salads, ./models/breakfast, ./models/gtea
4. Run python main.py --action=predict --dataset=50salads/gtea/breakfast --split=1/2/3/4/5 to generate predicted results for each split.
5. Run python eval.py --dataset=50salads/gtea/breakfast --split=0/1/2/3/4/5 to evaluate the performance. **NOTE**: split=0 will evaulate the average results for all splits, It needs to be done after you complete all split predictions.

python main.py --action=train --dataset=50salads/gtea/breakfast --split=1/2/3/4/5

@inproceedings{chinayi_ASformer,  
	author={Fangqiu Yi and Hongyu Wen and Tingting Jiang}, 
	booktitle={The British Machine Vision Conference (BMVC)},   
	title={ASFormer: Transformer for Action Segmentation},
	year={2021},  
}