GitHub - NVIDIA/partialconv: A New Padding Scheme: Partial Convolution based Pad...
source link: https://github.com/NVIDIA/partialconv
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.MD
Partial Convolution Layer for Padding and Image Inpainting
Padding Paper | Inpainting Paper | Inpainting YouTube Video
This is the PyTorch implementation of partial convolution layer. It can serve as a new padding scheme; it can also be used for image inpainting.
Partial Convolution based Padding
Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro
NVIDIA Corporation
Technical Report (Technical Report) 2018
Image Inpainting for Irregular Holes Using Partial Convolutions
Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro
NVIDIA Corporation
In The European Conference on Computer Vision (ECCV) 2018
Comparison with Zero Padding
Comparison with Zero Padding, Reflection Padding and Replication Padding for 5 runs
The best top-1 accuracies for each run with 1-crop testing. *_zero, *_pd, *_ref and *_rep indicate the corresponding model with zero padding, partial convolution based padding, reflection padding and replication padding respectively. *_best means the best validation score for each run of the training. Average represents the average accuracy of the 5 runs. Column diff represents the difference with corresponding network using zero padding. Column stdev represents the standard deviation of the accuracies from 5 runs. PT_official represents the corresponding official accuracies published on PyTorch website: https://pytorch.org/docs/stable/torchvision/models.html
Installation
Installation can be found: https://github.com/pytorch/examples/tree/master/imagenet
Usage:
#typical convolution layer with zero padding
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
#partial convolution based padding
PartialConv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
ResNet50 using zero padding (default padding)
python main.py -a resnet50 --data_train /path/ILSVRC/Data/CLS-LOC/train --data_val /path/ILSVRC/Data/CLS-LOC/perfolder_val --batch-size 192 --workers 32 --prefix multigpu_b192 --ckptdirprefix experiment_1/
ResNet50 using partial conv based padding
python main.py -a pdresnet50 --data_train /path/ILSVRC/Data/CLS-LOC/train --data_val /path/ILSVRC/Data/CLS-LOC/perfolder_val --batch-size 192 --workers 32 --prefix multigpu_b192 --ckptdirprefix experiment_1/
vgg16_bn using zero padding (default padding)
python main.py -a vgg16_bn --data_train /path/ILSVRC/Data/CLS-LOC/train --data_val /path/ILSVRC/Data/CLS-LOC/perfolder_val --batch-size 192 --workers 32 --prefix multigpu_b192 --ckptdirprefix experiment_1/
vgg16_bn using partial conv based padding
python main.py -a pdvgg16_bn --data_train /path/ILSVRC/Data/CLS-LOC/train --data_val /path/ILSVRC/Data/CLS-LOC/perfolder_val --batch-size 192 --workers 32 --prefix multigpu_b192 --ckptdirprefix experiment_1/
Citation
@inproceedings{liu2018partialpadding,
author = {Guilin Liu and Kevin J. Shih and Ting-Chun Wang and Fitsum A. Reda and Karan Sapra and Zhiding Yu and Andrew Tao and Bryan Catanzaro},
title = {Partial Convolution based Padding},
booktitle = {arXiv preprint arXiv:1811.11718},
year = {2018},
}
@inproceedings{liu2018partialinpainting,
author = {Guilin Liu and Fitsum A. Reda and Kevin J. Shih and Ting-Chun Wang and Andrew Tao and Bryan Catanzaro},
title = {Image Inpainting for Irregular Holes Using Partial Convolutions},
booktitle = {The European Conference on Computer Vision (ECCV)},
year = {2018},
}
Contact: Guilin Liu ([email protected])
Acknowledgments
We thank Jinwei Gu, Matthieu Le, Andrzej Sulecki, Marek Kolodziej and Hongfu Liu for helpful discussions.
Recommend
-
147
TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Understanding Conv...
-
104
convolution-visualizer - Convolution visualizations
-
47
README.md CoordConv for Keras Keras implementation of CoordConv from the paper An intriguing failing of convolutional neural networks and the CoordCon...
-
41
In this post we are going to walk through the details and intuition behind simple convolution operation as one one of the most fundamental concept in Computer Vision. Additionally we will build a
-
49
In this post we are going to walk through the details and intuition behind simple convolution operation as one one of the most fundamental concept in Computer Vision. Additionally we will build a Java Application GUI which uses different convolu...
-
42
Convolution: An Exploration of a Familiar Operator’s Deeper Roots In the world of modern machine learning, the convolution operator occupies the strange position: it’s both trivially familiar to anyon...
-
27
Convolution arithmetic A technical report on convolution arithmetic in the context of deep learning. The code and/or the images of this tutorial are free to use for non-commercial purposes with proper...
-
65
README.md PartialConv-Tensorflow Simple Tensorflow implementation of Partial Convolution based Padding
-
313
README.md FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [Project]...
-
4
Copy link Contributor hkratz commented
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK