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Detectron-ZZNet

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ZZNet in Detectron

Detectron-ZZNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation

by Di Lin, Dingguo Shen, Siting Shen,Yuanfeng Ji,Dani Lischinski,Daniel Cohen-Or,Hui Huang

## Introduction

This repository re-implements ZigZagNet on the base of [Detectron](https://github.com/facebookresearch/Detectron). Very consistent gains are available for all tested models, regardless of baseline strength.

Please follow [Detectron](https://github.com/facebookresearch/Detectron) on how to install and use ZigZagNet.

## Citation

If you use our code/model/data, please cite our paper:

```
@inproceedings{cai18cascadercnn,
  author = {Di Lin, Dingguo Shen, Siting Shen,Yuanfeng Ji,Dani Lischinski,Daniel Cohen-Or,Hui Huang},
  Title = {ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation},
  booktitle = {CVPR},
  Year  = {2019}
}
```

and Detectron:

```
@misc{Detectron2018,
  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and
                  Piotr Doll\'{a}r and Kaiming He},
  title =        {Detectron},
  howpublished = {\url{https://github.com/facebookresearch/detectron}},
  year =         {2018}
}
```

## Benchmarking

### End-to-End Mask R-CNN Baselines

All models were tested on the `coco_2017_val` dataset

<table align="canter"><tbody>



<th valign="bottom">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;backbone&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th valign="bottom">type</th>
<th valign="bottom">lr schd</th>
<th valign="bottom">im/ gpu</th>
<th valign="bottom">box AP</th>
<th valign="bottom">box AP50</th>
<th valign="bottom">box AP75</th>
<th valign="bottom">mask AP</th>
<th valign="bottom">mask AP50</th>
<th valign="bottom">mask AP75</th>
 <th valign="bottom">download links </th>

<tr>
<td align="left">X-101-64x4d-FPN-baseline</td>
<td align="left">Mask</td>
<td align="left">1x</td>
<td align="right">1</td>
<td align="right">42.38</td>
<td align="right">64.3</td>
<td align="right">46.41</td>
<td align="right">37.53</td>
<td align="right">60.63</td>
<td align="right">39.85</td>
<td align="center">——</td>
</tr>
<tr>
<td align="left">X-101-64x4d-FPN-ZZNet</td>
<td align="left">Mask</td>
<td align="left">1x</td>
<td align="right">1</td>
<td align="right">44.57</td>
<td align="right">66.66</td>
<td align="right">48.68</td>
<td align="right">39.59</td>
<td align="right">63.49</td>
<td align="right">41.96</td>
<td align="center"><a href="https://drive.google.com/file/d/1-rpMhTCssuDhowqdC2wqa7WTqybi-dHR/view?usp=sharing">model</a></td>
</tr>

<tr>
<td align="left">X-152-32x8d-FPN-baseline</td>
<td align="left">Mask</td>
<td align="left">1x</td>
<td align="right">1</td>
<td align="right">45.30</td>
<td align="right">66.85</td>
<td align="right">49.85</td>
<td align="right">39.75</td>
<td align="right">63.54</td>
<td align="right">42.25</td>
<td align="center">——</td>
</tr>
<tr>
<td align="left">X-152-32x8d-FPN-ZZNet</td>
<td align="left">Mask</td>
<td align="left">1x</td>
<td align="right">1</td>
<td align="right">46.44</td>
<td align="right">67.69</td>
<td align="right">51.15</td>
<td align="right">40.96</td>
<td align="right">64.88</td>
<td align="right">43.66</td>
<td align="center">model</td>
</tr>

</tbody></table>

## Model
The model weights that can reproduce numbers in our paper will be coming soon.

Detectron-ZZNet

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