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https://github.com/researchmm/Stark
[ICCV'21] Learning Spatio-Temporal Transformer for Visual Tracking
https://github.com/researchmm/Stark
transformer
Last synced: 6 days ago
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[ICCV'21] Learning Spatio-Temporal Transformer for Visual Tracking
- Host: GitHub
- URL: https://github.com/researchmm/Stark
- Owner: researchmm
- License: mit
- Created: 2021-03-25T06:52:43.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T07:02:29.000Z (7 months ago)
- Last Synced: 2024-08-02T06:13:14.596Z (3 months ago)
- Topics: transformer
- Language: Python
- Homepage:
- Size: 6.98 MB
- Stars: 631
- Watchers: 15
- Forks: 141
- Open Issues: 71
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# STARK
The official implementation of the **ICCV2021** paper [**Learning Spatio-Temporal Transformer for Visual Tracking**](https://openaccess.thecvf.com/content/ICCV2021/papers/Yan_Learning_Spatio-Temporal_Transformer_for_Visual_Tracking_ICCV_2021_paper.pdf)
Hiring research interns for visual transformer projects: [email protected]
## News
- STARK has been integrated into the [mmtracking](https://github.com/open-mmlab/mmtracking/tree/master/configs/sot/stark) library!
- :trophy: **We are the Winner of VOT-21 RGB-D challenge**
- :trophy: **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** :zap: . 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](lib/tutorials/STARK_Lightning_En.md)/[中文教程](lib/tutorials/STARK_Lightning_Ch.md)
- The raw results of STARK and other trackers on NOTU (NFS, OTB100, TC128, UAV123) have been uploaded to [here](https://drive.google.com/file/d/1KbtTdxxvvtC6_rlBM3Gi_H7HzpCdrX1F/view?usp=sharing)
## 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](https://hub.docker.com/repository/docker/alphabin/stark)
## 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](external/AR/README.md)
```
cd external/vot20/
export PYTHONPATH=:$PYTHONPATH
bash exp.sh
```
- VOT2020-LT
```
cd external/vot20_lt/
export PYTHONPATH=:$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](MODEL_ZOO.md)
## Acknowledgments
* Thanks for the great [PyTracking](https://github.com/visionml/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](https://github.com/facebookresearch/detr).