67 lines
No EOL
2.5 KiB
Markdown
67 lines
No EOL
2.5 KiB
Markdown
<div align="center">
|
|
<h1> Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour </h1>
|
|
|
|
**[Guanhua Zhang][4], [Zhiming Hu][5], [Mihai Bâce][3], [Andreas Bulling][6]** <br>
|
|
**ACM CHI 2024**, Honolulu, Hawaii <br>
|
|
**[[Project][2]]** **[[Paper][7]]** </div>
|
|
----------------
|
|
|
|
# Setup
|
|
We recommend to setup a virtual environment using Anaconda. <br>
|
|
1. Create a conda environment and install dependencies
|
|
```shell
|
|
conda env create --name mouse2vec --file=env.yaml
|
|
conda activate mouse2vec
|
|
```
|
|
2. Clone our repository to download our code and a pretrained model
|
|
```shell
|
|
git clone this_repo.git
|
|
```
|
|
|
|
# Run the code
|
|
Our code supports training using GPUs or CPUs. It will prioritise GPUs if available (line 45 in main.py). You can also assign a particular card via CUDA_VISIBLE_DEVICES (e.g., the following commands use GPU card no.3).
|
|
## Train Mouse2Vec Autoencoder<br>
|
|
Execute
|
|
```shell
|
|
CUDA_VISIBLE_DEVICES=3 python main.py --ssl True
|
|
```
|
|
## Use Mouse2Vec for Downstream Tasks
|
|
|
|
We enable two ways to use Mouse2Vec on your datasets for downstream tasks.
|
|
1. Use the (frozen) pretrained model and only train a MLP-based classifier <br>
|
|
|
|
Execute
|
|
```shell
|
|
CUDA_VISIBLE_DEVICES=3 python main.py --ssl True --load True --stage 0 --testDataset [Your Dataset]
|
|
```
|
|
|
|
2. Finetune both Mouse2Vec and the classifier <br>
|
|
|
|
Execute
|
|
```shell
|
|
CUDA_VISIBLE_DEVICES=3 python main.py --ssl True --load True --stage 1 --testDataset [Your Dataset]
|
|
```
|
|
|
|
# Citation
|
|
If you find our code useful or use it in your own projects, please cite our paper:
|
|
```
|
|
@inproceedings{zhang24_chi,
|
|
title = {Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour},
|
|
author = {Zhang, Guanhua and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas},
|
|
year = {2024},
|
|
pages = {1--17},
|
|
booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)},
|
|
doi = {10.1145/3613904.3642141}
|
|
}
|
|
```
|
|
|
|
# Acknowledgements
|
|
Our work relied on the codebase of [Cross Reconstruction Transformer][1]. Thanks to the authors for sharing their code.
|
|
|
|
[1]: https://github.com/BobZwr/Cross-Reconstruction-Transformer
|
|
[2]: https://perceptualui.org/publications/zhang24_chi/
|
|
[3]: https://scholar.google.com/citations?user=ku-t0MMAAAAJ&hl=en&oi=ao
|
|
[4]: https://scholar.google.com/citations?user=NqkK0GwAAAAJ&hl=en
|
|
[5]: https://scholar.google.com/citations?hl=en&user=OLB_xBEAAAAJ
|
|
[6]: https://www.perceptualui.org/people/bulling/
|
|
[7]: https://perceptualui.org/publications/zhang24_chi.pdf |