# piwise **Repository Path**: shirlim/piwise ## Basic Information - **Project Name**: piwise - **Description**: No description available - **Primary Language**: Unknown - **License**: BSD-3-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-06-09 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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.

Input	Output	Ground Truth