DPINet

This project hosts the code for implementing the DPINet algorithm for visual tracking.

The raw results are here. The code based on the PySOT.

Installation

Please find installation instructions in INSTALL.md.

Quick Start: Using DPINet

Add DPINetto your PYTHONPATH

export PYTHONPATH=/path/to/DPINet:$PYTHONPATH

Download models

Download models from here and put the model.pth in the correct directory in experiments

Webcam demo

python tools/demo.py \
    --config experiments/vot/config.yaml \
    --snapshot experiments/vot/model.pth
    # --video demo/bag.avi # (in case you don't have webcam)

Download testing datasets

Download datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from here. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.

Test tracker

cd experiments/vot
python -u ../../tools/test.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file

The testing results will in the current directory(results/dataset/model_name/)

Eval tracker

assume still in experiments/vot

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset VOT2018        \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'model'   # tracker_name

Training 🔧

See TRAIN.md for detailed instruction.

License

This project is released under the Apache 2.0 license.