metadata
license: other
tags:
- visual-object-tracking
- rgbt-tracking
- pytorch
ESMTrack
Model weights for ESMTrack.
Files
| File | Benchmark |
|---|---|
checkpoints/LasHeR_best_checkpoint.pth |
LasHeR |
checkpoints/VTUAV_best_checkpoint.pth |
VTUAV |
checkpoints/GTOT_best_checkpoint.pth |
GTOT |
checkpoints/RGBT210_best_checkpoint.pth |
RGBT210 |
checkpoints/RGBT234_best_checkpoint.pth |
RGBT234 |
Usage
Download the checkpoints into the project root:
hf download ShenglanLiaaa/ESMTrack --include "checkpoints/*" --local-dir .
Then test with the checkpoint of the corresponding benchmark, e.g. LasHeR:
python tracking/test.py --tracker_name esmtrack --tracker_param dropmae_256_150ep --checkpoint checkpoints/LasHeR_best_checkpoint.pth --dataset_name lasher --threads 8 --num_gpus 2
Pretrained backbone weights (e.g. DropMAE dropmae_k700_800E.pth) are not redistributed here; please download them from the DropMAE authors and put them under pretrained_networks/ (see the GitHub README).
If you find our work useful in your research, please consider citing:
@inproceedings{ESMTrack2026,
title={End-to-End Self-Supervised RGB-T Tracking without Modality Misleading},
author={Shenglan Li and Rui Yao and Kunyang Sun and Hong Jia and Yong Zhou and Javen Qinfeng Shi and Xinyu Zhang},
year={2026},
eprint={2609.37162},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.37162}
}