STEAL
This is the official inference code for:
David Acuna, Amlan Kar, Sanja Fidler
CVPR 2019 [Paper] [Project Page]
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of NVIDIA CORPORATION nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
git clone https://github.com/nv-tlabs/STEALcd STEAL
This code requires PyTorch 0.4 and python 3+. Please install dependencies by
pip install -r requirments.txt
Download the tar of the pretrained models from the Google Drive Folder, save it in 'checkpoints/', and run
cd checkpoints tar -xvf checkpoints.tar.gzcd ../
python inference_sbd.py --root_dir_val= ./data/sbd/data_aug/ --flist_val= ./data/sbd/data_aug/val_list.txt --output_folder=./output/sbd/ --ckpt=./checkpoints/sbd/model_checkpoint.pt
Instructions and preprocessing scripts to download SBD and preprocess the dataset can be found here: https://github.com/Chrisding/sbd-preprocess
python inference_cityscapes.py --root_dir_val=./data/cityscapes-preprocess/data_proc --flist_val=./data_proc/val.txt --output_folder=./output/cityscapes/ --ckpt=./checkpoints/cityscapes/model_checkpoint.pt
Instructions and preprocessing scripts for Cityscapes can be found here: https://github.com/Chrisding/cityscapes-preprocess
Test-NMS: An example of how to apply TEST-NMS using Piotr's Structured Forest matlab toolbox. can be found in utils/edges_nms.m
. During training, we optimized for the same set of operations with r=2 (Check paper for more details)
Checkout the ipython notebook that provides a simple walkthrough demonstrating how to run our model to refine coarsely annotated data.
If you use this code, please cite:
@inproceedings{AcunaCVPR19STEAL, title={Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations}, author={David Acuna and Amlan Kar and Sanja Fidler}, booktitle={CVPR}, year={2019} }
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