PyMDNet

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A tensorflow implementation of [Hyeonseob Nam and Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, CVPR2016]

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# PyMDNet
by [HungWei-Andy](https://github.com/HungWei-Andy) @ [NTU DISPLab](http://disp.ee.ntu.edu.tw/)

* [Introduction](#introduction)
* [Requirements](#requirements)
* [Data Directory](#data-directory)
* [Initial VGG-M Model](#initial-vgg-m-model)
* [Pretrained Model](#pretrained-model)
* [Usage](#usage)

## Introduction
Python (tensorflow) implementation of **[Hyeonseob Nam](https://kr.linkedin.com/in/hyeonseob-nam/) and [Bohyung Han](http://cvlab.postech.ac.kr/%7Ebhhan/), [Learning Multi-Domain Convolutional Neural Networks](https://arxiv.org/abs/1510.07945), CVPR2016**.

## Requirements
```bash
Python 2.7
numpy>=1.12.1
tensorflow-gpu==1.0.0
matplotlib>=2.0.1
skimage>=0.13.0
Pillow>=2.2.1
```

## Data Directory
Please download and put the video files of [OTB](http://cvlab.hanyang.ac.kr/tracker_benchmark/benchmark_v10.html) and [VOT](http://www.votchallenge.net/) in the directory 'data' with structure as follows:

.
    ├── data
    |     ├── otb
    |     |    ├── Basketball
    |     |    └── ...
    |     | 
    |     └── vot
    |          ├── vot2013
    |          |      ├── bicycle
    |          |      └── ...
    |          | 
    |          ├── vot2014
    |          |      ├── ball
    |          |      └── ...
    |          |
    |          └── vot2015
    |                 ├── bag
    |                 └── ...
    |
    ├── models
    └── README.md

## Initial VGG-M Model
The initial model is converted from [caffe VGG-M model](https://gist.github.com/ksimonyan/f194575702fae63b2829) into .npy file using [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) library provided by [ethereon](https://github.com/ethereon).

To download the initial model, run download.sh directly.
```bash
  bash download.sh
```

## Pretrained Models
We have pretrained the mdoel on vot dataset, otb dataset, and both. To download the pretrained model, run download_trained.sh directly.
```bash
  bash download_trained.sh
```

## Usage
### Tracking
```bash
  python tracking.py --dataset dataset --seq sequence --load_path path [--no_display]
```
 
### Pretraining
``` bash
  bash pretrain.sh
```

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