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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/).