# SwinTrack

**Repository Path**: frankxia2018_admin/SwinTrack

## Basic Information

- **Project Name**: SwinTrack
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-17
- **Last Updated**: 2022-01-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SwinTrack

This is the official repo for [SwinTrack](https://arxiv.org/abs/2112.00995).

![banner](https://raw.githubusercontent.com/wiki/LitingLin/SwinTrack/images/banner.svg)
## A Simple and Strong Baseline
![performance](https://raw.githubusercontent.com/wiki/LitingLin/SwinTrack/images/perf_efficiency_plot.svg)
## Prerequisites
### Environment
#### conda (recommended)
```shell
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
```shell
pip install -r requirements.txt
```
### Dataset
#### Download
- [LaSOT & LaSOT Extension](https://github.com/HengLan/LaSOT_Evaluation_Toolkit)
- [GOT-10k](http://got-10k.aitestunion.com/downloads)
- [TrackingNet](https://github.com/SilvioGiancola/TrackingNet-devkit)
- [COCO 2017](https://cocodataset.org/#download)
#### 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.
```yaml
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](https://drive.google.com/file/d/12vO2B-eWzP0JAjKG-j4hY97Plx-jhz9C/view?usp=sharing) or [baidu pan](https://pan.baidu.com/s/1m8HoUUj04b-uw1ATHuXDHg) (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](https://wandb.ai/), then login with command:
```shell
wandb login
```
## Training & Evaluation
### Train and evaluate on a single GPU
```shell
# Tiny
python main.py SwinTrack Tiny --output_dir /path/to/output --num_workers $num_dataloader_workers

# 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
```shell
# 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
```
### Train and evaluate on multiple nodes with multiple GPUs using DDP
```shell
# 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
```shell
# 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.
```shell
# 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
```shell
python main.py SwinTrack Tiny --mixin_config got10k.yaml
```
Submit ```$output_dir/test_metrics/got10k/submit/*.zip``` to the [GOT-10k evaluation server](http://got-10k.aitestunion.com/) to get the result of GOT-10k test split.
## Evaluate Existing Model
Download the pretrained model from [google drive](https://drive.google.com/drive/folders/1zPlgAs9D20g04_RWPPgTUg2j0C6A7adJ) or [baidu pan](https://pan.baidu.com/s/1CJ9laLTWMfa7HbleRGpwrw) (passcode: 8hsv), then type:
```shell
python main.py SwinTrack Tiny --weight_path /path/to/weigth_file.pth --mixin_config evaluation.yaml --output_dir /path/to/output
```
Our code can evaluate the model on multiple GPUs in parallel, so all parameters above are also available.
## Tracking results
Raw results: [google drive](https://drive.google.com/file/d/1JOJY5F2JuYG0Z-uqcP6cNV4ESGs5ek6z/view?usp=sharing) or [baidu pan](https://pan.baidu.com/s/1EBu3gf6nJLidYcccOUOlMA) (passcode: neyk)

[PyTracking](https://github.com/visionml/pytracking) compatible: [google drive](https://drive.google.com/file/d/1zCzuXbT0Vdas52yuDRAZIJ-k4MmtUf1w/view?usp=sharing) or [baidu pan](https://pan.baidu.com/s/1JKPOoW9L5fh1ShWyswD3Eg) (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}
}
```