Metadata-Version: 1.1
Name: pytorch-semseg
Version: 0.1.1
Summary: Semantic Segmentation Architectures implemented in PyTorch
Home-page: https://github.com/meetshah1995/pytorch-semseg
Author: Meet Pragnesh Shah
Author-email: meetshah1995@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: # pytorch-semseg
        
        [![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/meetshah1995/pytorch-semseg/blob/master/LICENSE)
        
        ## Semantic Segmentation Algorithms Implemented in PyTorch
        
        This repository aims at mirroring popular semantic segmentation architectures in PyTorch. 
        
        
        <p align="center">
        <a href="https://www.youtube.com/watch?v=iXh9aCK3ubs" target="_blank"><img src="https://i.imgur.com/agvJOPF.gif" width="364"/></a>
        <img src="https://meetshah1995.github.io/images/blog/ss/ptsemseg.png" width="49%"/>
        </p>
        
        
        ### Networks implemented
        
        * [PSPNet](https://arxiv.org/abs/1612.01105) - With support for loading pretrained models w/o caffe dependency
        * [FRRN](https://arxiv.org/abs/1611.08323) - Model A and B
        * [FCN](https://arxiv.org/abs/1411.4038) - All 1 (FCN32s), 2 (FCN16s) and 3 (FCN8s) stream variants
        * [U-Net](https://arxiv.org/abs/1505.04597) - With optional deconvolution and batchnorm
        * [Link-Net](https://codeac29.github.io/projects/linknet/) - With multiple resnet backends
        * [Segnet](https://arxiv.org/abs/1511.00561) - With Unpooling using Maxpool indices
        
        
        #### Upcoming 
        
        * [E-Net](https://arxiv.org/abs/1606.02147)
        * [RefineNet](https://arxiv.org/abs/1611.06612)
        
        ### DataLoaders implemented
        
        * [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
        * [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/segexamples/index.html)
        * [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/)
        * [MIT Scene Parsing Benchmark](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip)
        * [Cityscapes](https://www.cityscapes-dataset.com/)
        
        #### Upcoming
        
        * [NYUDv2](http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
        * [Sun-RGBD](http://rgbd.cs.princeton.edu/)
        * [MS COCO](http://mscoco.org/)
        
        ### Requirements
        
        * pytorch >=0.3.0
        * torchvision ==0.2.0
        * visdom >=1.0.1 (for loss and results visualization)
        * scipy
        * tqdm
        
        #### One-line installation
            
        `pip install -r requirements.txt`
        
        ### Data
        
        * Download data for desired dataset(s) from list of URLs [here](https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html#sec_datasets).
        * Extract the zip / tar and modify the path appropriately in `config.json`
        
        
        ### Usage
        
        Launch [visdom](https://github.com/facebookresearch/visdom#launch) by running (in a separate terminal window)
        
        ```
        python -m visdom.server
        ```
        
        **To train the model :**
        
        ```
        python train.py [-h] [--arch [ARCH]] [--dataset [DATASET]]
                        [--img_rows [IMG_ROWS]] [--img_cols [IMG_COLS]]
                        [--n_epoch [N_EPOCH]] [--batch_size [BATCH_SIZE]]
                        [--l_rate [L_RATE]] [--feature_scale [FEATURE_SCALE]]
                        [--visdom [VISDOM]]
        
          --arch           Architecture to use ['fcn8s, unet, segnet etc']
          --dataset        Dataset to use ['pascal, camvid, ade20k etc']
          --img_rows       Height of the input image
          --img_cols       Width of the input image
          --n_epoch        # of the epochs
          --batch_size     Batch Size
          --l_rate         Learning Rate
          --feature_scale  Divider for # of features to use
          --visdom         Show visualization(s) on visdom | False by default
        ```
        
        **To validate the model :**
        
        ```
        python validate.py [-h] [--model_path [MODEL_PATH]] [--dataset [DATASET]]
                           [--img_rows [IMG_ROWS]] [--img_cols [IMG_COLS]]
                           [--batch_size [BATCH_SIZE]] [--split [SPLIT]]
        
          --model_path   Path to the saved model
          --dataset      Dataset to use ['pascal, camvid, ade20k etc']
          --img_rows     Height of the input image
          --img_cols     Width of the input image
          --batch_size   Batch Size
          --split        Split of dataset to validate on
        ```
        
        **To test the model w.r.t. a dataset on custom images(s):**
        
        ```
        python test.py [-h] [--model_path [MODEL_PATH]] [--dataset [DATASET]]
                       [--dcrf [DCRF]] [--img_path [IMG_PATH]] [--out_path [OUT_PATH]]
         
          --model_path          Path to the saved model
          --dataset             Dataset to use ['pascal, camvid, ade20k etc']
          --dcrf                Enable DenseCRF based post-processing
          --img_path            Path of the input image
          --out_path            Path of the output segmap
        ```
        
Keywords: semantic-segmentation,fully-convolutional-networks,deep-learning,pytorch
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
