This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 0.4.1/1.2.0, CUDA 9.0. Please install related libraries before running this code:

pip install -r requirements.txt

Download the pretrained model:
general_model code: xjpz
got10k_model code: p4zx
LaSOT_model code: 6wer
and put them into tools/snapshot directory.

Download testing datasets and put them into test_dataset directory. Jsons of commonly used datasets can be downloaded from BaiduYun. If you want to test the tracker on a new dataset, please refer to pysot-toolkit to set test_dataset.

python test.py                                \
	--dataset UAV123                      \ # dataset_name
	--snapshot snapshot/general_model.pth  # tracker_name

The testing result will be saved in the results/dataset_name/tracker_name directory.

Download the datasets：

• VID
• YOUTUBEBB
• DET
• COCO
• GOT-10K
• LaSOT

Note: train_dataset/dataset_name/readme.md has listed detailed operations about how to generate training datasets.

Download pretrained backbones from google drive or BaiduYun (code: 7n7d) and put them into pretrained_models directory.

To train the SiamCAR model, run train.py with the desired configs:

cd tools
python train.py

We provide the tracking results (code: 71va) of GOT10K, LaSOT, OTB and UAV. If you want to evaluate the tracker, please put those results into results directory.

python eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset UAV123                  \ # dataset_name
	--tracker_prefix 'general_model'   # tracker_name

The code is implemented based on pysot. We would like to express our sincere thanks to the contributors.

If you use SiamCAR in your work please cite our paper:

@article{guo2019siamcar,
title={SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking},
author={Dongyan Guo and Jun Wang and Ying Cui and Zhenhua Wang and Shengyong Chen},
booktitle={CVPR},
year={2020}
}