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https://github.com/nv-tlabs/steal
STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)
https://github.com/nv-tlabs/steal
annotation cvpr2019 deep-learning devil-is-in-the-edges nv-tlabs pytorch semantic-boundaries semantic-segmentation steal
Last synced: 5 days ago
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STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)
- Host: GitHub
- URL: https://github.com/nv-tlabs/steal
- Owner: nv-tlabs
- Created: 2019-04-15T21:53:33.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-23T16:32:14.000Z (about 1 year ago)
- Last Synced: 2024-04-20T12:39:17.970Z (7 months ago)
- Topics: annotation, cvpr2019, deep-learning, devil-is-in-the-edges, nv-tlabs, pytorch, semantic-boundaries, semantic-segmentation, steal
- Language: Jupyter Notebook
- Homepage: https://nv-tlabs.github.io/STEAL/
- Size: 42.1 MB
- Stars: 475
- Watchers: 18
- Forks: 67
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# STEAL
This is the official inference code for:
#### Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations
[David Acuna](http://www.cs.toronto.edu/~davidj/), [Amlan Kar](http://www.cs.toronto.edu/~amlan/), [Sanja Fidler](http://www.cs.toronto.edu/~fidler/)
CVPR 2019
**[[Paper](https://arxiv.org/abs/1904.07934)] [[Project Page](https://nv-tlabs.github.io/STEAL/)]**
## License
```
# 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.
```
## Usage
##### Clone this repo
```bash
git clone https://github.com/nv-tlabs/STEAL
cd STEAL
```
#### Install dependencies
This code requires PyTorch 0.4 and python 3+. Please install dependencies by
```
pip install -r requirments.txt
```
#### Download pretrained models
Download the tar of the pretrained models from the [Google Drive Folder](https://drive.google.com/open?id=15IrPfMe9ZXJ4g0UV7tcA-LWPzCIPc1Sr), save it in 'checkpoints/', and run
```bash
cd checkpoints
tar -xvf checkpoints.tar.gz
cd ../
```
#### Inference (SBD)
```
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
#### Inference (Cityscapes)
```
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](https://github.com/pdollar/edges). 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)
#### Coarse-to-fine Demo
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}
}
```