DCFNet_pytorch.zip_DCFnet_pytorch_ECO目标跟踪 机器视觉_social1k3_torch马丁

# DCFNet_pytorch_{([arXiv](https://arxiv.org/pdf/1704.04057.pdf))} This repository contains a Python *reimplementation* of the [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf). ### Why implementation in python (PyTorch)? - Magical **Autograd** mechanism via PyTorch. Do not need to know the complicated BP. - Fast Fourier Transforms (**FFT**) supported by PyTorch 0.4.0. - Engineering demand. - Fast test speed (**120 FPS** on GTX 1060) and **Multi-GPUs** training. ### Contents 1. [Requirements](#requirements) 2. [Test](#test) 3. [Train](#train) 4. [Citing DCFNet](#citing-dcfnet) ## Requirements ```shell git clone --depth=1 https://github.com/foolwood/DCFNet_pytorch ``` Requirements for **PyTorch 0.4.0** and opencv-python ```shell conda install pytorch torchvision -c pytorch conda install -c menpo opencv ``` Training data (VID) and Test dataset (OTB). ## Test ```shell cd DCFNet_pytorch/track ln -s /path/to/your/OTB2015 ./dataset/OTB2015 ln -s ./dataset/OTB2015 ./dataset/OTB2013 cd dataset & python gen_otb2013.py python DCFNet.py ``` ## Train 1. Download training data. ([**ILSVRC2015 VID**](http://bvisionweb1.cs.unc.edu/ilsvrc2015/download-videos-3j16.php#vid)) ``` ./ILSVRC2015 ├── Annotations │   └── VID├── a -> ./ILSVRC2015_VID_train_0000 │ ├── b -> ./ILSVRC2015_VID_train_0001 │ ├── c -> ./ILSVRC2015_VID_train_0002 │ ├── d -> ./ILSVRC2015_VID_train_0003 │ ├── e -> ./val │ ├── ILSVRC2015_VID_train_0000 │ ├── ILSVRC2015_VID_train_0001 │ ├── ILSVRC2015_VID_train_0002 │ ├── ILSVRC2015_VID_train_0003 │ └── val ├── Data │   └── VID...........same as Annotations └── ImageSets └── VID ``` 2. Prepare training data for `dataloader`. ```shell cd DCFNet_pytorch/train/dataset python parse_vid.py <VID_path> # save all vid info in a single json python gen_snippet.py # generate snippets python crop_image.py # crop and generate a json for dataloader ``` 3. Training. (on multiple ***GPUs*** :zap: :zap: :zap: :zap:) ``` cd DCFNet_pytorch/train/ CUDA_VISIBLE_DEVICES=0,1,2,3 python train_DCFNet.py ``` ## Fine-tune hyper-parameter 1. After training, you can simple test the model with default parameter. ```shell cd DCFNet_pytorch/track/ python DCFNet --model ../train/work/crop_125_2.0/checkpoint.pth.tar ``` 2. Search a better hyper-parameter. ```shell CUDA_VISIBLE_DEVICES=0 python tune_otb.py # run on parallel to speed up searching python eval_otb.py OTB2013 * 0 10000 ``` ## Citing DCFNet If you find [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf) useful in your research, please consider citing: ``` @article{wang2017dcfnet, title={DCFNet: Discriminant Correlation Filters Network for Visual Tracking}, author={Wang, Qiang and Gao, Jin and Xing, Junliang and Zhang, Mengdan and Hu, Weiming}, journal={arXiv preprint arXiv:1704.04057}, year={2017} } ```