arXiv)
DCFNet_pytorch(This repository contains a Python reimplementation of the DCFNet.
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
Requirements
git clone --depth=1 https://github.com/foolwood/DCFNet_pytorch
Requirements for PyTorch 0.4.0 and opencv-python
conda install pytorch torchvision -c pytorch
conda install -c menpo opencv
Training data (VID) and Test dataset (OTB).
Test
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
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Download training data. (ILSVRC2015 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
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Prepare training data for
dataloader
.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
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Training. (on multiple GPUs ⚡ ⚡ ⚡ ⚡)
cd DCFNet_pytorch/train/ CUDA_VISIBLE_DEVICES=0,1,2,3 python train_DCFNet.py
Fine-tune hyper-parameter
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After training, you can simple test the model with default parameter.
cd DCFNet_pytorch/track/ python DCFNet --model ../train/work/crop_125_2.0/checkpoint.pth.tar
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Search a better hyper-parameter.
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 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}
}