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README.md

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-
-# HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization (ECCV 2024) 
-### Sakib Reza, Yuexi Zhang, Mohsen Moghaddam, Octavia Camps
-#### Northeastern University, Boston, United States
-{reza.s,zhang.yuex,mohsen,o.camps}@northeastern.edu
-
-## [Arxiv Preprint](https://arxiv.org/abs/2408.06437) 
-
-
-## Updates
-- Aug 22, 2024 - EGTEA pre-extracted features and config files for other datasets added
-- Aug 14, 2024 - Arxiv preprint added
-- July 7, 2024 - initial code release 
-
-## Installation
-
-### Prerequisites
-- Ubuntu 20.04 
-- Python 3.10.9 
-- CUDA 12.0  
-
-### Requirements
-- pytorch==2.0.0
-- numpy==1.23.5
-- h5py==3.9.0
-- ...
-
-To install all required libraries, execute the pip command below.
-```
-pip install -r requirement.txt
-```
-
-## Training
-
-### Input Features
-The Kinetics I3D pre-trained feature of EGTEA dataset can be downloaded from [GDrive link](https://drive.google.com/drive/folders/1Zj1B2UZnjPgLrylhKOfu7m_9rkQFa14T?usp=sharing).   
-Files should be located in 'data/'.  
-You can get other features from the following links -  
-- [EPIC-Kitchen 100](https://github.com/happyharrycn/actionformer_release)
-- [THUMOS'14](https://github.com/YHKimGithub/OAT-OSN/)
-- [MUSES](https://songbai.site/muses/)
-
-### Config Files
-The configuration files for EGTEA are already provided in the repository. For other datasets, they can be downloaded from [GDrive link](https://drive.google.com/drive/folders/19__GnM2HZCCDshED9kadsLNAI9XBvrFd?usp=sharing).
-
-### Training Model 
-To train the main HAT model, execute the command below.
-```
-python main.py --mode=train --split=[split #]*
-
-```
-```
-!python main.py --mode=train --batch_size=256 --epoch=1
-```
-*If the dataset has any splits (e.g., EGTEA has 4 splits)
-
-To train the post-processing network (OSN), execute the commands below.
-```
-python supnet.py --mode=make --inference_subset=train --split=[split #]
-python supnet.py --mode=make --inference_subset=test --split=[split #]
-python supnet.py --mode=train --split=[split #]
-```
-
-
-## Testing
-To test HAT, execute the command below.
-```
-python main.py --mode=test --split=[split #]
-```
-
-```
-!python main.py --mode=test --batch_size=256 --epoch=1
-
-```
-
-## Citing HAT
-Please cite our paper in your publications if it helps your research:
-
-```BibTeX
-@inproceedings{reza2022history,
-  title={HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization},
-  author={Reza, Sakib and Zhang, Yuexi and Moghaddam, Mohsen and Camps, Octavia},
-  booktitle={European Conference on Computer Vision},
-  pages={XXX--XXX},
-  year={2024},
-  organization={Springer}
-}
-```
-
-## Acknowledgment
-This repository is created based on the repository of the baseline work [OAT-OSN](https://github.com/YHKimGithub/OAT-OSN/).
+FROM python:3.9
+WORKDIR /app
+COPY . .
+RUN apt-get update && apt-get install -y ffmpeg
+RUN pip install -r requirements.txt
+CMD ["python", "main.py", "--gradio", "True"]