TransT | Transformer Tracking (CVPR2021) | Computer Vision library

This document contains detailed instructions for installing the necessary dependencied for TransT. The instructions have been tested on Ubuntu 18.04 system. You can modify these files to set the paths to datasets, results paths etc.
Create and activate a conda environment conda create -n transt python=3.7 conda activate transt
Install PyTorch conda install -c pytorch pytorch=1.5 torchvision=0.6.1 cudatoolkit=10.2
Install other packages conda install matplotlib pandas tqdm pip install opencv-python tb-nightly visdom scikit-image tikzplotlib gdown conda install cython scipy sudo apt-get install libturbojpeg pip install pycocotools jpeg4py pip install wget yacs pip install shapely==1.6.4.post2
Setup the environment Create the default environment setting files. # Change directory to <PATH_of_TransT> cd TransT # Environment settings for pytracking. Saved at pytracking/evaluation/local.py python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()" # Environment settings for ltr. Saved at ltr/admin/local.py python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"
Add the project path to environment variables Open ~/.bashrc, and add the following line to the end. Note to change <path_of_TransT> to your real path. export PYTHONPATH=<path_of_TransT>:$PYTHONPATH
Download the pre-trained networks Download the network for TransT and put it in the directory set by "network_path" in "pytracking/evaluation/local.py". By default, it is set to pytracking/networks.
We integrated PySOT for evaluation. You can download json files in PySOT or here. You need to specify the path of the model and dataset in the test.py. Then run the following commands. The testing results will in the current directory(results/dataset/transt/). You can also use pytracking to test and evaluate tracker. The results might be slightly different with PySOT due to the slight difference in implementation (pytracking saves results as integers, pysot toolkit saves the results as decimals). If you meet problem, please try searching our Github issues, if you can't find solutions, feel free to open a new issue. Solution: You can just delete from pysot_toolkit.toolkit.utils.region import vot_overlap, vot_float2str in test.py if you don't test VOT2019/18/16. You can also build region by python setup.py build_ext --inplace in pysot_toolkit.
Modify local.py to set the paths to datasets, results paths etc.
Runing the following commands to train the TransT. You can customize some parameters by modifying transt.py conda activate transt cd TransT/ltr python run_training.py transt transt
We integrated PySOT for evaluation. You can download json files in PySOT or here. You need to specify the path of the model and dataset in the test.py. net_path = '/path_to_model' #Absolute path of the model dataset_root= '/path_to_datasets' #Absolute path of the datasets Then run the following commands. conda activate TransT cd TransT python -u pysot_toolkit/test.py --dataset <name of dataset> --name 'transt' #test tracker #test tracker python pysot_toolkit/eval.py --tracker_path results/ --dataset <name of dataset> --num 1 --tracker_prefix 'transt' #eval tracker The testing results will in the current directory(results/dataset/transt/)
You can also use pytracking to test and evaluate tracker. The results might be slightly different with PySOT due to the slight difference in implementation (pytracking saves results as integers, pysot toolkit saves the results as decimals).
ImportError: cannot import name region