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https://github.com/LiYunfengLYF/LightFC
https://github.com/LiYunfengLYF/LightFC
Last synced: 6 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/LiYunfengLYF/LightFC
- Owner: LiYunfengLYF
- License: mit
- Created: 2023-10-08T11:44:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-12T13:16:21.000Z (10 months ago)
- Last Synced: 2024-08-02T06:12:23.990Z (3 months ago)
- Language: Python
- Size: 178 KB
- Stars: 26
- Watchers: 1
- Forks: 2
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# LightFC
The official implementation of LightFC
## News
- 14 Oct 2023: our code is available now
- 09 Oct 2023: our manuscript have submitted to [arxiv](https://arxiv.org/abs/2310.05392)
- 12 Jan 2024: lightfc-vit with higher performance is released !
## Install the environment
**Option1**: Use the Anaconda
```
conda create -n lightfc python=3.9
conda activate lightfc
bash install.sh
```
## Data Preparation
Follow [stark](https://github.com/researchmm/Stark) and [ostrack](https://github.com/botaoye/OSTrack) frameworks to set your datasets
## File directory
Project file directory should be like
```
${YOUR_PROJECT_ROOT}
-- experiments
|-- lightfc
-- external
|-- vot20st
-- lib
|--models
...
-- outputs (download and unzip the output.zip to obtain our checkpoints and row results)
|--checkpoints
|--...
|--test
|--...
-- pretrained_models (if you want to train lightfc, put pretrained model here)
|--mobilenetv2.pth (from torchvision model)
...
-- tracking
...
```
Download lightfc checkpoint and raw results at [Google Drive](https://drive.google.com/file/d/1ns7NQJCt078547X483skqjX1qM1rBqLP/view)
Download lightfc-vit checkpoint and raw results at [Google Drive](https://drive.google.com/file/d/1tckIW9P0RFheAAoGoSZR9Lgnet7-HNOL/view?usp=sharing)
Then go to these two files, and modify the paths
```
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
```
## Train LightFC
Training with multiple GPUs using DDP
```
python tracking/train.py --script LightFC --config mobilnetv2_p_pwcorr_se_scf_sc_iab_sc_adj_concat_repn33_se_conv33_center_wiou --save_dir . --mode multiple --nproc_per_node 2
```
If you want to train lightfc, please download https://download.pytorch.org/models/mobilenet_v2-b0353104.pth rather than https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth
if you want to train lightfc-vit, please download https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/tiny_vit_5m_22k_distill.pth
## Test and evaluate LightFC on benchmarks
Go to **tracking/test.py** and modify the parameters
```
python tracking/test.py
```
Then go to **tracking/analysis_results.py** and modify the parameters
```
python tracking/analysis_results.py
```
## Test FLOPs, Params, and Speed
```
# Params and FLOPs
python tracking/profile_model.py
# Speed
python tracking/speed.py
```
## Acknowledgments
* Thanks for the great [stark](https://github.com/researchmm/Stark) and [ostrack](https://github.com/botaoye/OSTrack) Libraries, which helps us to quickly implement our ideas.