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https://github.com/LitingLin/SwinTrack
https://github.com/LitingLin/SwinTrack
Last synced: 11 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/LitingLin/SwinTrack
- Owner: LitingLin
- License: mit
- Created: 2021-11-21T10:55:39.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-18T11:13:35.000Z (about 2 years ago)
- Last Synced: 2024-08-01T03:44:03.361Z (3 months ago)
- Language: Python
- Size: 582 KB
- Stars: 233
- Watchers: 5
- Forks: 40
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# SwinTrack
This is the official repo for [SwinTrack](https://arxiv.org/abs/2112.00995).
Update: Our work is accepted by NeurIPS 2022🎇, new model weight & tracking raw results: [google drive](https://drive.google.com/drive/folders/12lbs_A6Y1v6AtqfM6qoTaxPXUU4pk1wz?usp=sharing). arxiv link is updated. Code will be updated soon.
## A Simple and Strong Baseline
## 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~~
Follow the updated link.
~~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}
}
```