Context Encoders: Feature Learning by Inpainting

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

1) Semantic Inpainting Demo

1. Install PyTorch http://pytorch.org/

2. Clone the repository

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

3. 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 iamgespython 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

1. 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

2. Train

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

1. Test

   This step is similar to Semantic Inpainting Demo