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https://github.com/goutamyg/SMAT
[WACV 2024] Separable Self and Mixed Attention Transformers for Efficient Object Tracking
https://github.com/goutamyg/SMAT
mixed-attention self-attention single-object-tracking visual-object-tracking visual-tracking wacv wacv2024
Last synced: about 2 months ago
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[WACV 2024] Separable Self and Mixed Attention Transformers for Efficient Object Tracking
- Host: GitHub
- URL: https://github.com/goutamyg/SMAT
- Owner: goutamyg
- License: apache-2.0
- Created: 2023-08-17T19:26:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-02T21:36:49.000Z (5 months ago)
- Last Synced: 2024-06-17T00:27:22.461Z (3 months ago)
- Topics: mixed-attention, self-attention, single-object-tracking, visual-object-tracking, visual-tracking, wacv, wacv2024
- Language: Python
- Homepage:
- Size: 1.81 MB
- Stars: 23
- Watchers: 3
- Forks: 4
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# [Separable Self and Mixed Attention Transformers for Efficient Object Tracking](https://openaccess.thecvf.com/content/WACV2024/papers/Gopal_Separable_Self_and_Mixed_Attention_Transformers_for_Efficient_Object_Tracking_WACV_2024_paper.pdf) [WACV2024]
# Official implementation
## News
**`09-04-2024`**: C++ implementation of SMAT is available [here](https://github.com/goutamyg/MVT.cpp)
**`07-09-2023`**: The paper is available on [arXiv](https://arxiv.org/abs/2309.03979) now
**`28-08-2023`**: The pretrained tracker model is released
**`17-08-2023`**: The SMAT tracker training and inference code is released
**`14-08-2023`**: The paper is accepted at WACV2024
## Installation
Install the dependency packages using the environment file `smat_pyenv.yml`.
Generate the relevant files:
```
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output
```
After running this command, modify the datasets paths by editing these files
```
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
```
## Training
* Set the path of training datasets in `lib/train/admin/local.py`
* Place the pretrained backbone model under the `pretrained_models/` folder
* For data preparation, please refer to [this](https://github.com/botaoye/OSTrack/tree/main)
* Uncomment lines `63, 67, and 71` in the [base_backbone.py](https://github.com/goutamyg/SMAT/blob/main/lib/models/mobilevit_track/base_backbone.py) file.
Long story short: The code is opitmized for high inference speed, hence some intermediate feature-maps are pre-computed during testing. However, these pre-computations are not feasible during training.
* Run
```
python tracking/train.py --script mobilevitv2_track --config mobilevitv2_256_128x1_ep300 --save_dir ./output --mode single
```
* The training logs will be saved under `output/logs/` folder
## Pretrained tracker model
The pretrained tracker model can be found [here](https://drive.google.com/drive/folders/1TindIEwu82IvtozwL4XQFrSnFE2Z6W4y)
## Tracker Evaluation
* Update the test dataset paths in `lib/test/evaluation/local.py`
* Place the [pretrained tracker model](https://drive.google.com/drive/folders/1TindIEwu82IvtozwL4XQFrSnFE2Z6W4y) under `output/checkpoints/` folder
* Run
```
python tracking/test.py --tracker_name mobilevitv2_track --tracker_param mobilevitv2_256_128x1_ep300 --dataset got10k_test or trackingnet or lasot
```
* Change the `DEVICE` variable between `cuda` and `cpu` in the `--tracker_param` file for GPU and CPU-based inference, respectively
* The raw results will be stored under `output/test/` folder
## Tracker demo
To evaluate the tracker on a sample video, run
```
python tracking/video_demo.py --tracker_name mobilevitv2_track --tracker_param mobilevitv2_256_128x1_ep300 --videofile *path-to-video-file* --optional_box *bounding-box-annotation*
```
## Visualization of tracker output and the attention maps
## Acknowledgements
* We use the Separable Self-Attention Transformer implementation and the pretrained `MobileViTv2` backbone from [ml-cvnets](https://github.com/apple/ml-cvnets). Thank you!
* Our training code is built upon [OSTrack](https://github.com/botaoye/OSTrack) and [PyTracking](https://github.com/visionml/pytracking)
* To generate the evaluation metrics for different datasets (except, server-based GOT-10k and TrackingNet), we use the [pysot-toolkit](https://github.com/StrangerZhang/pysot-toolkit)
## Citation
If our work is useful for your research, please consider citing:
```Bibtex
@inproceedings{gopal2024separable,
title={Separable self and mixed attention transformers for efficient object tracking},
author={Gopal, Goutam Yelluru and Amer, Maria A},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={6708--6717},
year={2024}
}
```