Update README.md

README.md

@@ -1,12 +1,14 @@
 # MS-TCN++: MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation (TPAMI 2020)
 
+This repository provides a PyTorch implementation of the paper [MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://arxiv.org/pdf/2006.09220.pdf).
+
 ## Environment
-Python3
+Python3, pytorch
 
 ## Training:
-Download the data folder, which contains the features and the ground truth labels. (~30GB) (If you cannot download the data from the previous link, try to download it from here)
-Extract it so that you have the data folder in the same directory as main.py.
-To train the model run sh train.sh ${dataset} ${split} where ${dataset} is breakfast, 50salads or gtea, and ${split} is the split number (1-5) for 50salads and (1-4) for the other datasets.
+* Download the [data](https://mega.nz/#!O6wXlSTS!wcEoDT4Ctq5HRq_hV-aWeVF1_JB3cacQBQqOLjCIbc8) folder, which contains the features and the ground truth labels. (~30GB) (If you cannot download the data from the previous link, try to download it from [here](https://zenodo.org/record/3625992#.Xiv9jGhKhPY))
+* Extract it so that you have the `data` folder in the same directory as `main.py`.
+* To train the model run sh train.sh ${dataset} ${split} where ${dataset} is breakfast, 50salads or gtea, and ${split} is the split number (1-5) for 50salads and (1-4) for the other datasets.
 
 ## Evaluation
 Run sh test_epoch.sh ${dataset} ${split} ${test_epoch}.
@@ -20,4 +22,13 @@ Run sh test_epoch.sh ${dataset} ${split} ${test_epoch}.
   journal={arXiv preprint arXiv:2006.09220},
   year={2020}
 }
+
+@inproceedings{farha2019ms,
+  title={Ms-tcn: Multi-stage temporal convolutional network for action segmentation},
+  author={Farha, Yazan Abu and Gall, Jurgen},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={3575--3584},
+  year={2019}
+}
+
 ```