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Pixel-wise segmentation on VOC2012 dataset using pytorch.
| Date | Stars |
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| 2026-07-24 | 387 |
| 2026-07-25 | 387 |
| 2026-07-28 | 387 |
| 2026-07-30 | 387 |
| 2026-08-06 | 387 |
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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>
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:eedd69e4327dc841, topic:computer-vision, readme:semantic segmentation
matched fp:eedd69e4327dc841, topic:pytorch