# SiamCAR

**Repository Path**: wh__tzz/SiamCAR

## Basic Information

- **Project Name**: SiamCAR
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2020-09-29
- **Last Updated**: 2021-01-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [SiamCAR](https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_SiamCAR_Siamese_Fully_Convolutional_Classification_and_Regression_for_Visual_Tracking_CVPR_2020_paper.html)

## 1. Environment setup
This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 0.4.1/1.2.0, CUDA 9.0.
Please install related libraries before running this code: 
```bash
pip install -r requirements.txt
```

## 2. Test
Download the pretrained model:  
[general_model](https://pan.baidu.com/s/1kIbKxCu1O3PXt9wQik4EVQ) code: xjpz  
[got10k_model](https://pan.baidu.com/s/1KSVgaz5KYP2Ar2DptnfyGQ) code: p4zx  
[LaSOT_model](https://pan.baidu.com/s/1g15wGSq-LoZUBxYQwXCP6w) code: 6wer  
 and put them into `tools/snapshot` directory.

Download testing datasets and put them into `test_dataset` directory. Jsons of commonly used datasets can be downloaded from [BaiduYun](https://pan.baidu.com/s/1js0Qhykqqur7_lNRtle1tA#list/path=%2F). If you want to test the tracker on a new dataset, please refer to [pysot-toolkit](https://github.com/StrangerZhang/pysot-toolkit) to set test_dataset.

```bash 
python test.py                                \
	--dataset UAV123                      \ # dataset_name
	--snapshot snapshot/general_model.pth  # tracker_name
```
The testing result will be saved in the `results/dataset_name/tracker_name` directory.

## 3. Train

### Prepare training datasets

Download the datasets：
* [VID](http://image-net.org/challenges/LSVRC/2017/)
* [YOUTUBEBB](https://pan.baidu.com/s/1gQKmi7o7HCw954JriLXYvg) (code: v7s6)
* [DET](http://image-net.org/challenges/LSVRC/2017/)
* [COCO](http://cocodataset.org)
* [GOT-10K](http://got-10k.aitestunion.com/downloads)
* [LaSOT](https://cis.temple.edu/lasot/)

**Note:** `train_dataset/dataset_name/readme.md` has listed detailed operations about how to generate training datasets.

### Download pretrained backbones
Download pretrained backbones from [google drive](https://drive.google.com/drive/folders/1DuXVWVYIeynAcvt9uxtkuleV6bs6e3T9) or [BaiduYun](https://pan.baidu.com/s/1IfZoxZNynPdY2UJ_--ZG2w) (code: 7n7d) and put them into `pretrained_models` directory.

### Train a model
To train the SiamCAR model, run `train.py` with the desired configs:

```bash
cd tools
python train.py
```

## 4. Evaluation
We provide the tracking [results](https://pan.baidu.com/s/1C_3MqKtZmLsMPWgqj-F3sg) (code: 71va) of GOT10K, LaSOT, OTB and UAV. If you want to evaluate the tracker, please put those results into  `results` directory.
```
python eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset UAV123                  \ # dataset_name
	--tracker_prefix 'general_model'   # tracker_name
```

## 5. Acknowledgement
The code is implemented based on [pysot](https://github.com/STVIR/pysot). We would like to express our sincere thanks to the contributors.


## 6. Cite
If you use SiamCAR in your work please cite our paper:
> @InProceedings{Guo_2020_CVPR,  
   author = {Guo, Dongyan and Wang, Jun and Cui, Ying and Wang, Zhenhua and Chen, Shengyong},  
   title = {SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking},  
   booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},  
   month = {June},  
   year = {2020}  
}