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https://github.com/goutamyg/MVT
[BMVC 2023] Mobile Vision Transformer-based Visual Object Tracking
https://github.com/goutamyg/MVT
bmvc bmvc2023 mobile-vision-transformer single-object-tracking vision-transformer visual-object-tracking visual-tracking
Last synced: about 2 months ago
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[BMVC 2023] Mobile Vision Transformer-based Visual Object Tracking
- Host: GitHub
- URL: https://github.com/goutamyg/MVT
- Owner: goutamyg
- License: apache-2.0
- Created: 2023-08-22T15:06:39.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-23T21:51:30.000Z (5 months ago)
- Last Synced: 2024-06-17T00:35:53.224Z (3 months ago)
- Topics: bmvc, bmvc2023, mobile-vision-transformer, single-object-tracking, vision-transformer, visual-object-tracking, visual-tracking
- Language: Python
- Homepage:
- Size: 22.9 MB
- Stars: 20
- Watchers: 2
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# [Mobile Vision Transformer-based Visual Object Tracking](https://papers.bmvc2023.org/0800.pdf) [BMVC2023] official implementation
## News
**`11-03-2024`**: C++ implementation of our tracker is [available now](https://github.com/goutamyg/MVT.cpp/tree/main)
**`10-11-2023`**: ONNX-Runtime and TensorRT-based inference code is released. Now, our ***MVT*** runs at ~70 *fps* on CPU and ~300 *fps* on GPU :zap::zap:. Check the [page](https://github.com/goutamyg/MVT/blob/multi_framework_inference/lib/tutorial/MVT_ONNX_TRT_Tutorial.md) for details.
**`14-09-2023`**: The pretrained tracker model is released
**`13-09-2023`**: The paper is available on [arXiv](https://arxiv.org/abs/2309.05829) now
**`22-08-2023`**: The MVT tracker training and inference code is released
**`21-08-2023`**: The paper is accepted at BMVC2023
## Installation
Install the dependency packages using the environment file `mvt_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/MVT/blob/main/lib/models/mobilevit_track/base_backbone.py) file. Replace [these lines](https://github.com/goutamyg/MVT/blob/main/lib/test/tracker/mobilevit_track.py#L68-L78) with ```self.z_dict1 = template.tensors```.
* Run
```
python tracking/train.py --script mobilevit_track --config mobilevit_256_128x1_got10k_ep100_cosine_annealing --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/1RAdn3ZXI_G7pBj4NDbtQVFPkClVd1IBm)
## Tracker Evaluation
* Update the test dataset paths in `lib/test/evaluation/local.py`
* Place the [pretrained tracker model](https://drive.google.com/drive/folders/1RAdn3ZXI_G7pBj4NDbtQVFPkClVd1IBm) under `output/checkpoints/` folder
* Run
```
python tracking/test.py --tracker_name mobilevit_track --tracker_param mobilevit_256_128x1_got10k_ep100_cosine_annealing --dataset got10k_test/trackingnet/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
## Profile tracker model
* To count the model parameters, run
```
python tracking/profile_model.py
```
## Acknowledgements
* We use the Separable Self-Attention Transformer implementation and the pretrained `MobileViT` 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)
## Citation
If our work is useful for your research, please consider citing:
```Bibtex
@inproceedings{Gopal_2023_BMVC,
author = {Goutam Yelluru Gopal and Maria Amer},
title = {Mobile Vision Transformer-based Visual Object Tracking},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0800.pdf}
}
```