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