STARK
The official implementation of the ICCV2021 paper Learning Spatio-Temporal Transformer for Visual Tracking
Hiring research interns for visual transformer projects: [email protected]
News
- STARK has been integrated into the mmtracking library!
- π We are the Winner of VOT-21 RGB-D challenge
- π We won the Runner-ups in VOT-21 Real-Time and Long-term challenges
- We release an extremely fast version of STARK called STARK-Lightning β‘ . It can run at 200~300 FPS on a RTX TITAN GPU. Besides, its performance can beat DiMP50, while the model size is even less than that of SiamFC! More details can be found at STARK_Lightning_En.md/δΈζζη¨
- The raw results of STARK and other trackers on NOTU (NFS, OTB100, TC128, UAV123) have been uploaded to here
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_pytorch17.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
Set project paths
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
# Profiling STARK-Lightning-X-trt
python tracking/profile_model_lightning_X_trt.py
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.