Metadata-Version: 1.1
Name: pytorch-semseg
Version: 0.1.2
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|
        
        Semantic Segmentation Algorithms Implemented in PyTorch
        -------------------------------------------------------
        
        This repository aims at mirroring popular semantic segmentation
        architectures in PyTorch.
        
        .. raw:: html
        
           <p align="center">
        
        .. raw:: html
        
           </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
        
        .. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg
           :target: https://github.com/meetshah1995/pytorch-semseg/blob/master/LICENSE
        
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
