PyTorch Implement of Context Encoders: Feature Learning by Inpainting

Last update: Dec 25, 2022

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Deep Learning context_encoder_pytorch

Overview

Context Encoders: Feature Learning by Inpainting

This is the Pytorch implement of CVPR 2016 paper on Context Encoders

1) Semantic Inpainting Demo

Install PyTorch http://pytorch.org/
Clone the repository

git clone https://github.com/BoyuanJiang/context_encoder_pytorch.git

Demo

Download pre-trained model on Paris Streetview from Google Drive OR BaiduNetdisk

cp netG_streetview.pth context_encoder_pytorch/model/
cd context_encoder_pytorch/model/
# Inpainting a batch iamges
python test.py --netG model/netG_streetview.pth --dataroot dataset/val --batchSize 100
# Inpainting one image 
python test_one.py --netG model/netG_streetview.pth --test_image result/test/cropped/065_im.png

2) Train on your own dataset

Build dataset

Put your images under dataset/train,all images should under subdirectory

dataset/train/subdirectory1/some_images

dataset/train/subdirectory2/some_images

...

Note:For Google Policy,Paris StreetView Dataset is not public data,for research using please contact with pathak22. You can also use The Paris Dataset to train your model
Train

python train.py --cuda --wtl2 0.999 --niter 200

Test

This step is similar to Semantic Inpainting Demo

PyTorch Implement of Context Encoders: Feature Learning by Inpainting

Related tags

Overview

Context Encoders: Feature Learning by Inpainting

1) Semantic Inpainting Demo

2) Train on your own dataset

Owner

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