[SiamRAAN]

1. Environment setup

This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 1.2.0, CUDA 11.0. Please install related libraries before running this code:

pip install -r requirements.txt

2. Test

Download the pretrained model:
model code: 867p
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 or Google driver. 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.

3. Train

Prepare training datasets

Download the datasets：

• VID
• YOUTUBEBB (code: v7s6)
• 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

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

Train a model

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

cd tools
python train.py

4. Evaluation

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

5. Acknowledgement

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