Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

License

Copyright (C) 2019 NVIDIA Corporation. Towaki Takikawa, David Acuna, Varun Jampani, Sanja FidlerAll rights reserved.Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).Permission to use, copy, modify, and distribute this software and its documentationfor any non-commercial purpose is hereby granted without fee, provided that the abovecopyright notice appear in all copies and that both that copyright notice and thispermission notice appear in supporting documentation, and that the name of the authornot be used in advertising or publicity pertaining to distribution of the softwarewithout specific, written prior permission.THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALLIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE.IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIALDAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISINGOUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.~

Usage

Clone this repo

git clone https://github.com/nv-tlabs/GSCNNcd GSCNN

Python requirements

Download pretrained models

Download inferred images

Evaluation (Cityscapes)

python train.py --evaluate --snapshot checkpoints/best_cityscapes_checkpoint.pth

Training

@article{takikawa2019gated,  title={Gated-SCNN: Gated Shape CNNs for Semantic Segmentation},  author={Takikawa, Towaki and Acuna, David and Jampani, Varun and Fidler, Sanja},  journal={ICCV},  year={2019}}