Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

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: about 2 months ago
JSON representation

STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

Lists

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/)]**



![STEAL DEMO](https://nv-tlabs.github.io/STEAL/resources/teaser_gif.gif)



## 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.



![Coarse to Fine](https://nv-tlabs.github.io/STEAL/resources/coarse_to_fine_g.gif)



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}

}

```