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A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network
| Date | Stars |
|---|---|
| 2026-07-24 | 439 |
| 2026-07-25 | 439 |
| 2026-07-28 | 439 |
| 2026-07-30 | 439 |
| 2026-08-06 | 439 |
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# Fast-SCNN: Fast Semantic Segmentation Network A PyTorch implementation of [Fast-SCNN: Fast Semantic Segmentation Network](https://arxiv.org/pdf/1902.04502) from the paper by Rudra PK Poudel, Stephan Liwicki. <p align="center"><img width="100%" src="./png/Fast-SCNN.png" /></p> ## Table of Contents - <a href='#installation'>Installation</a> - <a href='#datasets'>Datasets</a> - <a href='#training-fast-scnn'>Train</a> - <a href='#evaluation'>Evaluate</a> - <a href='#demo'>Demo</a> - <a href='#results'>Results</a> - <a href='#todo'>TO DO</a> - <a href='#references'>Reference</a> ## Installation - Python 3.x. Recommended using [Anaconda3](https://www.anaconda.com/distribution/) - [PyTorch 1.0](https://pytorch.org/get-started/locally/). 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](#datasets) below. - Note: For training, we currently support [cityscapes](https://www.cityscapes-dataset.com/), and aim to add [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/). ## Datasets - You can download [cityscapes](https://www.cityscapes-dataset.com/) from [here](https://www.cityscapes-dataset.com/downloads/). Note: please download [leftImg8bit_trainvaltest.zip(11GB)](https://www.cityscapes-dataset.com/file-handling/?packageID=4) and [gtFine_trainvaltest(241MB)](https://www.cityscapes-dataset.com/file-handling/?packageID=1). ## 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. ```Shell python train.py --model fast_scnn --dataset citys ``` ## Evaluation To evaluate a trained network: ```Shell python eval.py ``` ## Demo Running a demo: ```Shell 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. <img src="./png/frankfurt_000001_058914_leftImg8bit.png" width="280" /><img src="./png/frankfurt_000001_058914_gtFine_color.png" width="280" /><img src="./png/frankfurt_000001_058914_seg.png" width="280" />           (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 * [**Tramac**](https://github.com/Tramac) ## References - Rudra PK Poudel. et al. "Fast-SCNN: Fast Semantic Segmentation Network".
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matched fp:0229b4b1b70ea1eb, topic:deep-learning, topic:pytorch, readme:distributed training
matched fp:0229b4b1b70ea1eb, topic:computer-vision, desc:semantic segmentation, readme:semantic segmentation