Awesome
AFter
Attention-based Fusion Router for RGBT Tracking. arXiv
<div align="center"> <img style="width:85%;" src="https://img2.imgtp.com/2024/05/01/Qv646jYC.png"/> </div>Dataset
We use the LasHeR training set for training, GTOT, RGBT210, RGBT234, LasHeR testing set, VTUAVST for testing, and their project addresses are as follows:
Environment Preparation
Clone repo:
git clone https://github.com/Alexadlu/AFter.git
cd AFter
Our code is trained and tested with Python == 3.8, PyTorch == 1.8.1 and CUDA == 11.2 on NVIDIA GeForce RTX 4090, you may use a different version according to your GPU.
conda create -n after python=3.8.13
conda activate after
pip install -r requirements.txt
Training
- Modify the project path and dataset path in
$PROJECT_ROOT$/ltr/admin/local.py
. - Download ToMP-50 pretrained weights and put it under
$PROJECT_ROOT$/ltr/models/pretrained
. - Run the following command.
python ltr/run_training.py --train_module tomp --train_name tomp50_v1
Evaluation
- Modify the dataset path in
$PROJECT_ROOT$/pytracking/evaluation/environment.py
- Put the checkpoint into
$PROJECT_ROOT$/pytracking/networks
and select the checkpoint name in$PROJECT_ROOT$/pytracking/parameter/tomp/tomp50.py
. Or just modify the checkpoint path in$PROJECT_ROOT$/pytracking/parameter/tomp/tomp50.py
. - Run the following command.
python pytracking/run_tracker.py --tracker_name tomp --tracker_param tomp50 --runid 8600 --dataset_name lashertestingset
Results and Models
Model | GTOT(PR/SR) | RGBT210(PR/SR) | RGBT234(PR/SR) | LasHeR(PR/NPR/SR) | VTUAV(PR/SR) | Checkpoint | Raw Result |
---|---|---|---|---|---|---|---|
AFter | 91.6 / 78.5 | 87.6 / 63.5 | 90.1 / 66.7 | 70.3 / 65.8 / 55.1 | 84.9 / 72.5 | download | download |
Acknowledgments
Our project is based on the pytracking framework and ToMP. Thanks for their contributions which help us to quickly implement our ideas.