This is the official inference code for:
David Acuna, Amlan Kar, Sanja Fidler
CVPR 2019 [Paper] [Project Page]
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git clone https://github.com/nv-tlabs/STEAL
cd STEALThis 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.gz
cd ../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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