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piwise

(★ 387)

Pixel-wise segmentation on VOC2012 dataset using pytorch.

파일 탐색기

최종 버전 다운로드 (.zip)

- criterion.py
- dataset.py
- network.py
- transform.py
- visualize.py
- dataset.py
LICENSE.md
main.py
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/bodokaiser/piwise

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd piwise

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

python -m visdom.server -port 5000

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# PiWiSe

Pixel-wise segmentation on the [VOC2012][dataset] dataset using
[pytorch][pytorch].

- [x] [FCN](https://arxiv.org/abs/1605.06211)
- [x] [SegNet](https://arxiv.org/abs/1511.00561)
- [ ] [PSPNet](https://arxiv.org/abs/1612.01105)
- [x] [UNet](https://arxiv.org/abs/1505.04597)
- [ ] [RefineNet](https://arxiv.org/abs/1611.06612)

**For a more complete implementation of segmentation networks checkout [semseg](https://github.com/meetshah1995/pytorch-semseg).**

Note:

- FCN differs from original implementation see [this issue](https://github.com/bodokaiser/piwise/issues/4)
- SegNet does not match original paper performance see [here](https://github.com/bodokaiser/piwise/issues/3)
- PSPNet misses "atrous convolution" (conv layers of ResNet101 should be amended to preserve image size)

Keeping this in mind feel free to PR. Thank you!

## Setup

See dataset examples [here][dataset_example].

### Download

Download [image archive][dataset_download] and extract and do:

```
mkdir data
mv VOCdevkit/VOC2012/JPEGImages data/images
mv VOCdevkit/VOC2012/SegmentationClass data/classes
rm -rf VOCdevkit
```

### Install

We recommend using [pyenv][pyenv]:

```
pyenv virtualenv 3.6.0 piwise
pyenv activate piwise
```

then install requirements with `pip install -r requirements.txt`.

## Usage

For latest documentation use:

```
python main.py --help
```

Supported model parameters are `fcn8`, `fcn16`, `fcn32`, `unet`, `segnet1`,
`segnet2`, `pspnet`.

### Training

If you want to have visualization open an extra tab with:

```
python -m visdom.server -port 5000
```

Train the SegNet model 30 epochs with cuda support, visualization
and checkpoints every 100 steps:

```
python main.py --cuda --model segnet2 train --datadir data \
    --num-epochs 30 --num-workers 4 --batch-size 4 \
    --steps-plot 50 --steps-save 100
```

### Evaluation

Then we want to do semantic segmentation on `foo.jpg`:

```
python main.py --model segnet2 --state segnet2-30-0 eval foo.jpg foo.png
```

The segmented class image can now be found at `foo.png`.

[pyenv]: https://github.com/pyenv/pyenv
[pytorch]: http://pytorch.org
[dataset]: http://host.robots.ox.ac.uk/pascal/VOC/
[dataset_example]: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/segexamples
[dataset_download]: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

### Results

These are some results based on segnet after 40 epoches. Set

```python
loss_weights[0] = 1 / 1
```
to deal gracefully with the unbalanced problem.

<table>
  <thead>
    <tr>
      <th>Input</th>
      <th>Output</th>
      <th>Ground Truth</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447927-c6c55200-091e-11e9-8b4e-65c84cd26f39.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447931-c75de880-091e-11e9-954c-a405f6ec4bbb.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447937-c7f67f00-091e-11e9-8d11-0f7f98def638.jpg">
      </td>
    </tr>
    <tr>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447928-c6c55200-091e-11e9-9c3f-cbd95918beb2.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447933-c75de880-091e-11e9-8c8f-de6ce6a1eebd.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447938-c7f67f00-091e-11e9-8d95-45c65ea21d57.jpg">
      </td>
    </tr>
    <tr>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447929-c75de880-091e-11e9-9386-608115753e88.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447934-c75de880-091e-11e9-811f-90e1843ced14.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447939-c7f67f00-091e-11e9-94e0-79c0ca87ee63.jpg">
      </td>
    </tr>
    <tr>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447930-c75de880-091e-11e9-9853-1a03c8b94878.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447935-c75de880-091e-11e9-9df2-78ccbc5866a4.jpg">
      </td>
      <td>
        <img src="https://user-images.githubusercontent.com/1780466/50447940-c7f67f00-091e-11e9-9b7d-6a351d489370.jpg">
      </td>
    </tr>
  </tbody>
</table>