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cong-wu authored Mar 21, 2024
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# SCD-Net
## An new SOTA method for Unsupervised Skeleton-based Action Understanding
(Code will be published soon!)
The Official implementation for 'SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition' (AAAI 2024).


- [Prerequisite](#Prerequisite)
- [Data](#Data)
- [Training&Testing](#Training&Testing)
- [Log files](#Log)


<a name="Prerequisite"></a>

# Prerequisite

- Pytorch

- We provided requirement file to install all packages, just by running


`pip install -r requirements.txt`


<a name="Data"></a>

# Data

## Generate the data

**Download the raw data**

- [NTU-RGB+D](https://rose1.ntu.edu.sg/dataset/actionRecognition/).
- [PKU-MMD](https://www.icst.pku.edu.cn/struct/Projects/PKUMMD.html).

**Preprocess**

- Preprocess data with `python ntu_gendata.py`.


<a name="Training&Testing"></a>

# Training&Testing

## Training

- To train on NTU-RGB+D 60 under Cross-Subject evaluation, you can run


python ./pretraining.py --lr 0.01 --batch-size 64 --encoder-t 0.2 --encoder-k 8192 \
--checkpoint-path ./checkpoints/pretrain/ \
--schedule 351 --epochs 451 --pre-dataset ntu60 \
--protocol cross_subject --skeleton-representation joint

## Testing


- For action recognition on NTU-RGB+D 60 under Cross-Subject evaluation, you can run


python ./action_classification.py --lr 2 --batch-size 1024 \
--pretrained ./checkpoints/pretrain/checkpoint.pth.tar \
--finetune-dataset ntu60 --protocol cross_subject --finetune_skeleton_representation joint

- For action retrieval on NTU-RGB+D 60 under Cross-Subject evaluation, you can run


python ./action_retrieval.py --knn-neighbours 1 \
--pretrained ./checkpoints/pretrain/checkpoint.pth.tar \
--finetune-dataset ntu60 --protocol cross_subject --finetune-skeleton-representation joint

<a name="Log"></a>

# Log files

We also provided some the testing logs in ./log
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