The official implementation for the CVPR 2025 paper
[Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking]
[Models], [Raw Results]
| Tracker | UAV123 (AUC) | UAV123_10FPS (AUC) | UAVDT (AUC) | DTB70 (AUC) | UAVTrack112 (AUC) | UAVTrack_L (AUC) |
|---|---|---|---|---|---|---|
| SGLATrack-DeiT* | 66.9 | 65.5 | 59.9 | 65.1 | 67.5 | 64.0 |
| SGLATrack-ViT | 66.1 | 64.5 | 60.0 | 65.8 | 67.3 | 64.3 |
| SGLATrack-EVA | 65.1 | 64.3 | 57.9 | 63.8 | 66.9 | 64.7 |
| Tracker | TrackingNet (AUC) | LaSOT (AUC) | GOT-10k (AO) |
|---|---|---|---|
| SGLATrack-DeiT* | 79.5 | 63.0 | 66.3 |
| SGLATrack-ViT | 79.4 | 64.1 | 66.0 |
| SGLATrack-EVA | 77.7 | 60.9 | 64.2 |
Put the training datasets in ./data. It should look like:
${PROJECT_ROOT}
-- data
-- lasot
|-- airplane
|-- basketball
|-- bear
...
-- got10k
|-- test
|-- train
|-- val
-- coco
|-- annotations
|-- images
-- trackingnet
|-- TRAIN_0
|-- TRAIN_1
...
|-- TRAIN_11
|-- TEST
For ease of testing, we have made the structured dataset available for download at here, code: 5vbv.
Put the test datasets in ./data. It should look like:
${PROJECT_ROOT}
-- data
-- UAV123
|-- anno
|-- data_seq
-- UAV123_10fps
|-- anno
|-- data_seq
-- uavdt
|-- anno
|-- sequences
-- V4RFlight112
|-- anno
|-- anno_l
|-- data_seq
|-- attributes
-- DTB70
|-- Animal1
|-- Animal2
...
-- VisDrone2018-SOT-test-dev
|-- annotations
|-- sequences
|-- attributes
Run the following command to set paths for this project
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output
After running this command, you can also modify paths by editing these two files
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
Download pre-trained DeiT-tiny distilled weights and put it under $PROJECT_ROOT$/pretrained_models
python tracking/train.py \
--script sglatrack --config deit_distilled \
--save_dir ./output \
--mode multiple --nproc_per_node 4 \
--use_wandb 0
Replace --config with the desired model config under experiments/sglatrack.
We use wandb to record detailed training logs, in case you don't want to use wandb, set --use_wandb 0.
- UAV123 or other off-line evaluated benchmarks (modify
--datasetcorrespondingly)
python tracking/test.py --tracker_param sglatrack --dataset uav123 --threads 8 --num_gpus 4
python tracking/analysis_results.py # need to modify tracker configs and names
- uav123_10fps
python tracking/test.py --tracker_param sglatrack --dataset uav123_10fps --threads 8 --num_gpus 4
- uavtrack_L
python tracking/test.py --tracker_param sglatrack --dataset uavtrack --threads 8 --num_gpus 4
- uavtrack112
python tracking/test.py --tracker_param sglatrack --dataset uavtrack112 --threads 8 --num_gpus 4
- uavdt
python tracking/test.py --tracker_param sglatrack --dataset uavdt --threads 8 --num_gpus 4
- dtb70
python tracking/test.py --tracker_param sglatrack --dataset dtb70 --threads 8 --num_gpus 4
- visdrone
python tracking/test.py --tracker_param sglatrack --dataset visdrone --threads 8 --num_gpus 4
Note: The speeds reported in our paper were tested on a single RTX2080Ti GPU.
python tracking/profile_model.py
For any questions or cooperation, please contact xcc23cg@163.com or wechat: chaocan23
If our work is useful for your research, please consider citing:
@inproceedings{sglatrack,
title={Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking},
author={Xue, Chaocan and Zhong, Bineng and Liang, Qihua and Zheng, Yaozong and Li, Ning and Xue, Yuanliang and Song, Shuxiang},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6730-6740}
}