Docker environment is also provided in the root directory.
The code was tested on Ubuntu 16.04, with Anaconda Python 3.6, CUDA 10.0, and PyTorch v1.0. It should be compatible with PyTorch <=1.4 and python >=0.4 (you will need to switch DCNv2 version for PyTorch <1.0). After installing Anaconda:
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[Optional but highly recommended] create a new conda environment.
conda create --name SearchTrack python=3.6
And activate the environment.
conda activate SearchTrack
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Install PyTorch:
conda install pytorch torchvision -c pytorch
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Install COCOAPI:
pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
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Clone this repo:
SearchTrack_ROOT=/path/to/clone/SearchTrack git clone --recursive https://github.com/qa276390/SearchTrack $SearchTrack_ROOT
You can manually install the submodules if you forget
--recursive
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Install the requirements
pip install -r requirements.txt
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Compile deformable convolutional (from DCNv2).
cd $SearchTrack_ROOT/src/lib/model/networks/ # git clone https://github.com/CharlesShang/DCNv2/ # clone if it is not automatically downloaded by `--recursive`. cd DCNv2 ./make.sh
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Download pertained models(NOT READY YET) and move them to
$SearchTrack_ROOT/models/
. More models can be found in Model zoo.