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All Projects → Tramac → Fast Scnn Pytorch

Tramac / Fast Scnn Pytorch

Licence: apache-2.0
A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network

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deep-learningpytorchcomputer-visionsemantic-segmentation

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Fast-SCNN: Fast Semantic Segmentation Network

A PyTorch implementation of Fast-SCNN: Fast Semantic Segmentation Network from the paper by Rudra PK Poudel, Stephan Liwicki.

Table of Contents

Installation

  • Python 3.x. Recommended using Anaconda3
  • PyTorch 1.0. Install PyTorch by selecting your environment on the website and running the appropriate command. Such as: 
    conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
  • Clone this repository.
  • Download the dataset by following the instructions below.
  • Note: For training, we currently support cityscapes, and aim to add VOC and ADE20K.

Datasets

Training-Fast-SCNN

  • By default, we assume you have downloaded the cityscapes dataset in the ./datasets/citys dir.
  • To train Fast-SCNN using the train script the parameters listed in train.py as a flag or manually change them.
python train.py --model fast_scnn --dataset citys

Evaluation

To evaluate a trained network:

python eval.py

Demo

Running a demo:

python demo.py --model fast_scnn --input-pic './png/berlin_000000_000019_leftImg8bit.png'

Results

Method Dataset crop_size mIoU pixAcc
Fast-SCNN(paper) cityscapes
Fast-SCNN(ours) cityscapes 768 54.84% 92.37%

Note: The result based on crop_size=768, which is different with paper.

          (a) test image            (b) ground truth           (c) predicted result

TODO

  • [ ] add distributed training
  • [ ] Support for the VOC, ADE20K dataset
  • [ ] Support TensorBoard
  • [x] save the best model
  • [x] add Ohem Loss

Authors

References

  • Rudra PK Poudel. et al. "Fast-SCNN: Fast Semantic Segmentation Network".
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