STARK

The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking

Hiring research interns for visual transformer projects: [email protected]

Highlights

End-to-End, Post-processing Free

STARK is an end-to-end tracking approach, which directly predicts one accurate bounding box as the tracking result.
Besides, STARK does not use any hyperparameters-sensitive post-processing, leading to stable performances.

Real-Time Speed

STARK-ST50 and STARK-ST101 run at 40FPS and 30FPS respectively on a Tesla V100 GPU.

Strong performance

Tracker	LaSOT (AUC)	GOT-10K (AO)	TrackingNet (AUC)
STARK	67.1	68.8	82.0
TransT	64.9	67.1	81.4
TrDiMP	63.7	67.1	78.4
Siam R-CNN	64.8	64.9	81.2

Purely PyTorch-based Code

STARK is implemented purely based on the PyTorch.

Install the environment

Option1: Use the Anaconda

conda create -n stark python=3.6
conda activate stark
bash install.sh

Option2: Use the docker file

We provide the complete docker at here

Data Preparation

Put the tracking datasets in ./data. It should look like:

${STARK_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- coco
         |-- annotations
         |-- images
     -- trackingnet
         |-- TRAIN_0
         |-- TRAIN_1
         ...
         |-- TRAIN_11
         |-- TEST

Run the following command to set paths for this project

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .

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

Train STARK

Training with multiple GPUs using DDP

# STARK-S50
python tracking/train.py --script stark_s --config baseline --save_dir . --mode multiple --nproc_per_node 8  # STARK-S50
# STARK-ST50
python tracking/train.py --script stark_st1 --config baseline --save_dir . --mode multiple --nproc_per_node 8  # STARK-ST50 Stage1
python tracking/train.py --script stark_st2 --config baseline --save_dir . --mode multiple --nproc_per_node 8 --script_prv stark_st1 --config_prv baseline  # STARK-ST50 Stage2
# STARK-ST101
python tracking/train.py --script stark_st1 --config baseline_R101 --save_dir . --mode multiple --nproc_per_node 8  # STARK-ST101 Stage1
python tracking/train.py --script stark_st2 --config baseline_R101 --save_dir . --mode multiple --nproc_per_node 8 --script_prv stark_st1 --config_prv baseline_R101  # STARK-ST101 Stage2

(Optionally) Debugging training with a single GPU

python tracking/train.py --script stark_s --config baseline --save_dir . --mode single

Test and evaluate STARK on benchmarks

• LaSOT

python tracking/test.py stark_st baseline --dataset lasot --threads 32
python tracking/analysis_results.py # need to modify tracker configs and names

• GOT10K-test

python tracking/test.py stark_st baseline_got10k_only --dataset got10k_test --threads 32
python lib/test/utils/transform_got10k.py --tracker_name stark_st --cfg_name baseline_got10k_only

• TrackingNet

python tracking/test.py stark_st baseline --dataset trackingnet --threads 32
python lib/test/utils/transform_trackingnet.py --tracker_name stark_st --cfg_name baseline

• VOT2020
  Before evaluating "STARK+AR" on VOT2020, please install some extra packages following external/AR/README.md

cd external/vot20/<workspace_dir>
export PYTHONPATH=<path to the stark project>:$PYTHONPATH
bash exp.sh

• VOT2020-LT

cd external/vot20_lt/<workspace_dir>
export PYTHONPATH=<path to the stark project>:$PYTHONPATH
bash exp.sh

Test FLOPs, Params, and Speed

# Profiling STARK-S50 model
python tracking/profile_model.py --script stark_s --config baseline
# Profiling STARK-ST50 model
python tracking/profile_model.py --script stark_st2 --config baseline
# Profiling STARK-ST101 model
python tracking/profile_model.py --script stark_st2 --config baseline_R101

Model Zoo

The trained models, the training logs, and the raw tracking results are provided in the model zoo

Acknowledgments

• Thanks for the great PyTracking Library, which helps us to quickly implement our ideas.
• We use the implementation of the DETR from the official repo https://github.com/facebookresearch/detr.