[SiamAPN]

1. Environment setup

This code has been tested on Ubuntu 18.04, Python 3.8.3, Pytorch 0.7.0/1.6.0, CUDA 10.2. Please install related libraries before running this code:

pip install -r requirements.txt

2. Test

Download pretrained model (epoch=37) : general_model(code:w3u5) and put it into tools/snapshot directory.

Download testing datasets and put them into test_dataset directory. If you want to test the tracker on a new dataset, please refer to pysot-toolkit to set test_dataset.

python test.py                                \
	--dataset UAV10fps                      \ # 
    --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: t7j8)
• COCO
• GOT-10K

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

Train a model

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

cd tools
python train.py

4. Evaluation

We provide the tracking results (code: s3p1) of UAV123@10fps, UAV20L, and VisDrone2018-SOT-test. If you want to evaluate the tracker, please put those results into results directory.

python eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset UAV10fps                  \ # 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.