SwinTrack

This is the official repo for SwinTrack.

A Simple and Strong Baseline

Prerequisites

Environment

conda (recommended)

conda create -y -n SwinTrack
conda activate SwinTrack
conda install -y anaconda
conda install -y pytorch torchvision cudatoolkit -c pytorch
conda install -y -c fvcore -c iopath -c conda-forge fvcore
pip install wandb
pip install timm

pip

pip install -r requirements.txt

Dataset

Download

• LaSOT & LaSOT Extension
• GOT-10k
• TrackingNet
• COCO 2017

Unzip

The paths should be organized as following:

lasot
├── airplane
├── basketball
...
├── training_set.txt
└── testing_set.txt

lasot_extension
├── atv
├── badminton
...
└── wingsuit

got-10k
├── train
│   ├── GOT-10k_Train_000001
│   ...
├── val
│   ├── GOT-10k_Val_000001
│   ...
└── test
    ├── GOT-10k_Test_000001
    ...
    
trackingnet
├── TEST
├── TRAIN_0
...
└── TRAIN_11

coco2017
├── annotations
│   ├── instances_train2017.json
│   └── instances_val2017.json
└── images
    ├── train2017
    │   ├── 000000000009.jpg
    │   ├── 000000000025.jpg
    │   ...
    └── val2017
        ├── 000000000139.jpg
        ├── 000000000285.jpg
        ...

Prepare path.yaml

Copy path.template.yaml as path.yaml and fill in the paths.

LaSOT_PATH: '/path/to/lasot'
LaSOT_Extension_PATH: '/path/to/lasot_ext'
GOT10k_PATH: '/path/to/got10k'
TrackingNet_PATH: '/path/to/trackingnet'
COCO_2017_PATH: '/path/to/coco2017'

Prepare dataset metadata cache (optional)

Download the metadata cache from google drive or baidu pan (passcode: 5dt9), and unzip it in datasets/cache/

datasets
└── cache
    ├── SingleObjectTrackingDataset_MemoryMapped
    │   └── filtered
    │       ├── got-10k-got10k_vot_train_split-train-3c1ffeb0c530522f0345d088b2f72168.np
    │       ...
    └── DetectionDataset_MemoryMapped
        └── filtered
            └── coco2017-nocrowd-train-bcd5bf68d4b87619ab451fe293098401.np

Login to wandb

Register an account at wandb, then login with command:

wandb login

Training & Evaluation

Train and evaluate on a single GPU

# Tiny
python main.py SwinTrack Tiny --output_dir /path/to/output --num_workers $num_dataloader_workers
python main.py SwinTrack Tiny --output_dir ./output --num_workers 8

# Base
python main.py SwinTrack Base --output_dir /path/to/output --num_workers $num_dataloader_workers

# Base-384
python main.py SwinTrack Base-384 --output_dir /path/to/output --num_workers $num_dataloader_workers

--output_dir is optional, --num_workers defaults to 4.

note: our code performs evaluation automatically when training is done, output is saved in /path/to/output/test_metrics.

Train and evaluate on multiple GPUs using DDP

# Tiny
python main.py SwinTrack Tiny --distributed_nproc_per_node $num_gpus --distributed_do_spawn_workers --output_dir /path/to/output --num_workers $num_dataloader_workers
python main.py SwinTrack Tiny --distributed_nproc_per_node 3 --distributed_do_spawn_workers --output_dir output --num_workers 8

Train and evaluate on multiple nodes with multiple GPUs using DDP

# Tiny
python main.py SwinTrack Tiny --master_address $master_address --distributed_node_rank $node_rank distributed_nnodes $num_nodes --distributed_nproc_per_node $num_gpus --distributed_do_spawn_workers --output_dir /path/to/output --num_workers $num_dataloader_workers 

Train and evaluate with run.sh helper script

# Train and evaluate on all GPUs
./run.sh SwinTrack Tiny --output_dir /path/to/output -W $num_dataloader_workers
# Train and evaluate on multiple nodes
NODE_RANK=$NODE_INDEX NUM_NODES=$NUM_NODES MASTER_ADDRESS=$MASTER_ADDRESS DATE_WITH_TIME=$DATE_WITH_TIME ./run.sh SwinTrack Tiny --output_dir /path/to/output --num_workers $num_dataloader_workers 

Ablation study

The ablation study can be done by applying a small patch to the main config file.

Take the ResNet 50 backbone as the example, the rest parameters are the same as the above.

# Train and evaluate with resnet50 backbone
python main.py SwinTrack Tiny --mixin_config resnet.yaml
# or with run.sh
./run.sh SwinTrack Tiny --mixin resnet.yaml

All available config patches are listed in config/SwinTrack/Tiny/mixin.

Train and evaluate with GOT-10k dataset

python main.py SwinTrack Tiny --mixin_config got10k.yaml

Submit $output_dir/test_metrics/got10k/submit/*.zip to the GOT-10k evaluation server to get the result of GOT-10k test split.

Evaluate Existing Model

Download the pretrained model from google drive or baidu pan (passcode: 8hsv), then type:

python main.py SwinTrack Tiny --weight_path /path/to/weigth_file.pth --mixin_config evaluation.yaml --output_dir /path/to/output
python main.py SwinTrack Tiny --weight_path checkpoint/SwinTrack-Tiny-GOT.pth --mixin_config evaluation.yaml --output_dir ./output
python main.py SwinTrack Tiny --weight_path checkpoint/SwinTrack-Tiny.pth --mixin_config evaluation.yaml --output_dir ./output --wandb_run_offline

Our code can evaluate the model on multiple GPUs in parallel, so all parameters above are also available.

Tracking results

Raw results: google drive or baidu pan (passcode: neyk)

PyTracking compatible: google drive or baidu pan (passcode: w5fk)

Citation

@misc{lin2021swintrack,
      title={SwinTrack: A Simple and Strong Baseline for Transformer Tracking}, 
      author={Liting Lin and Heng Fan and Yong Xu and Haibin Ling},
      year={2021},
      eprint={2112.00995},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}