Use the Anaconda
conda create -n uitrack python=3.8
conda activate uitrack
bash install.sh
Put the tracking datasets in ./data. It should look like:
${UITrack_ROOT}
-- data
-- LaSOTBenchmark
|-- airplane
|-- basketball
|-- bear
...
-- tnl2k
-- train
|-- Arrow_Video_ZZ04_done
|-- Assassin_video_1-Done
...
-- test
|-- advSamp_Baseball_game_002-Done
|-- advSamp_Baseball_video_01-Done
...
-- OTB_sentences
|-- OTB_query_test
|-- OTB_query_train
|-- OTB_videos
-- refcoco
-- annotations
-- refcoco-unc
|-- instances.json
|-- ix_to_token.pkl
...
-- refcocog-google
...
|-- images
|--train2014
|--train2017
Run the following command to set paths for this project
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir output
After running this command, you can also modify paths by editing these two files
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
Download pre-trained MAE ViT-Base weights and put it under $PROJECT_ROOT$/pretrained_models (different pretrained models can also be used, see MAE for more details).
Training with multiple GPUs using DDP.
bash train.sh
Download the model weights and raw results from Baidu Netdisk.
- LaSOT/TNL2K/OTB99-L. More details of test settings can be found at
bash test.sh
python tracking/profile_model.py --config="uitrack_256_mae_32x4_ep100_prompt"
python tracking/profile_model.py --config="uitrack_384_mae_32x4_ep100_prompt"