AMNAR

KO | EN

gitlite — search

#python #hacktoberfest #typescript #llm #go #golang #machine-learning #deep-learning #nodejs #kubernetes #vue #pytorch #mcp #ios

(★ 12)

(CVPR 2025) Official implementation of Paper ''Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks''

AMNAR 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks

(CVPR 2025) Official implementation of Paper ''Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks''

CVPR version: [Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks](https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Modeling_Multiple_Normal_Action_Representations_for_Error_Detection_in_Procedural_CVPR_2025_paper.html)

arXiv version: [Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks](https://arxiv.org/abs/2503.22405)

If you find this paper helpful, please cite our work:

```plaintext
@article{huang2025modeling,
  title={Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks},
  author={Huang, Wei-Jin and Li, Yuan-Ming and Xia, Zhi-Wei and Tang, Yu-Ming and Lin, Kun-Yu and Hu, Jian-Fang and Zheng, Wei-Shi},
  journal={arXiv preprint arXiv:2503.22405},
  year={2025}
}
```

## 1 Prepare and Process Data

Download Datasets from official release: [EgoPER](https://github.com/robert80203/EgoPER_official), [HoloAssist](https://holoassist.github.io/), [CaptainCook4D](https://github.com/CaptainCook4D/downloader)

EgoPER Dataset File Structure

```plaintext
EgoPER/
├── coffee
│   ├── features_10fps_dinov2
│   ├── features_10fps_new
│   ├── frames_10fps_new
│   ├── test.txt
│   ├── training.txt
│   ├── trim_start_end.txt
│   ├── trim_videos
│   └── validation.txt
├── oatmeal
│   ├── features_10fps_dinov2
│   ├── features_10fps_new
│   ├── frames_10fps_new
│   ├── test.txt
│   ├── training.txt
│   ├── trim_start_end.txt
│   ├── trim_videos
│   └── validation.txt
├── ......
```

## 2 Training

A training example of `tea` task in EgoPER

- Train Action Segmentation Model (ActionFormer)

```bash
python train.py ./configs/EgoPER/tea_aod_online.yaml --output af200
```

- Train Error Detection Model

```bash
python train.py ./configs/EgoPER/tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200.yaml --resume ./ckpt/EgoPER/tea_aod_online_af200/epoch_205.pth.tar --output 1st 
```

Also, you can choose to download our weights to reproduce our work. [Google Drive](https://drive.google.com/drive/folders/1DrnDhNWq1MDmtFpjwOMz_VuQo5PeAD_a?usp=sharing)

## 3 Inference

An inference example of `tea` task in EgoPER

- Use Action Segmentation Model to get the segmentation result

```bash
python test.py ./configs/EgoPER/tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200.yaml ./ckpt/EgoPER/tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200_1st
```

- Use Error Detection Model to detect errors

```bash
python test_ed.py ./configs/EgoPER/tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200.yaml ./ckpt/EgoPER/tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200_1st
```

- Calculate Test Metrics

```
python metric_vis_multiprocess.py --task tea --dirname tea_aod_rebuild_ca_sigt_online_clusterCenter_it0.6_addedMax_norm_unfreeze_winlen32_dila3_dilaLayer5_fr5_cu10_cus0.40_m0.9_dp0.1_e200_1st/ -as -ed 
```

More examples can be found in `run_sh_EgoPER/run_clusterCenter_it0.6_addedMax_ca_usenorm_unfreeze_winlen32_dilation3_dilalayers5_fr5_cu10_cus0.40_m0.9_dp0.1_e200.sh`

## Note

The implementation of AMNAR's code is based on [ActionFormer](https://github.com/happyharrycn/actionformer_release) and [EgoPED](https://github.com/robert80203/EgoPER_official). We recommend reading the code of these two works to help with understanding.

AMNAR

AMNAR 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

AMNAR

AMNAR 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README