Note: For the -name option, please ensure your experiment name contains "domainA" or "domainB", which will be used to select different dataset. Python train_domain_B.py -continue_train -training_dataset domain_B -name domainB_old_photos -label_nc 0 -loadSize 256 -fineSize 256 -dataroot -no_instance -resize_or_crop crop_only -batchSize 120 -no_html -gpu_ids 0,1,2,3 -self_gen -nThreads 4 -n_downsample_global 3 -k_size 4 -use_v2 -mc 64 -start_r 1 -kl 1 -no_cgan -outputs_dir -checkpoints_dir Python train_domain_A.py -use_v2_degradation -continue_train -training_dataset domain_A -name domainA_SR_old_photos -label_nc 0 -loadSize 256 -fineSize 256 -dataroot -no_instance -resize_or_crop crop_only -batchSize 100 -no_html -gpu_ids 0,1,2,3 -self_gen -nThreads 4 -n_downsample_global 3 -k_size 4 -use_v2 -mc 64 -start_r 1 -kl 1 -no_cgan -outputs_dir -checkpoints_dir Put the folders of VOC dataset, collected old photos (e.g., Real_L_old and Real_RGB_old) into one shared folder. Exit window by clicking Exit Window and get your result image in output folder.Wait for a while and see results on GUI window.Click browse and select your image from test_images/old_w_scratch folder to remove scratches.5) GUIĪ user-friendly GUI which takes input of image by user and shows result in respective window. Since the model is pretrained with 256*256 images, the model may not work ideally for arbitrary resolution. This repo is mainly for research purpose and we have not yet optimized the running performance. More details could be found in our journal submission and. We use a progressive generator to refine the face regions of old photos. Python test.py -Scratch_and_Quality_restore \ InstallationĬlone the Synchronized-BatchNorm-PyTorch repository for The code is tested on Ubuntu with Nvidia GPUs and CUDA installed. You can now play with our Colab and try it on your photos. Training code is available and welcome to have a try and learn the training details. The framework now supports the restoration of high-resolution input. ![]() For more details, please refer to the project website and github repo. Otherwise, you can subscribe to any of the four plans for in-depth research of your ancestors. If you want to build just a primary ancestors lineage, the free plan will be sufficient. Deep Nostalgia Apk is a Books & Reference application for Android that is simply the greatest approach to learn more about your history. Old Photo Restoration via Deep Latent Space Translation, TPAMI 2022ġCity University of Hong Kong, 2Microsoft Research Asia, 3Microsoft Cloud AI, 4USTC ✨ NewsĢ022.3.31: Our new work regarding old film restoration will be published in CVPR 2022. You can use MyHeritage to discover new relatives, upload, enhance and animate your family photos. Project Page | Paper (CVPR version) | Paper (Journal version) | Pretrained Model | Colab Demo | Replicate Demo & Docker Image □īringing Old Photos Back to Life, CVPR2020 (Oral) Old Photo Restoration (Official PyTorch Implementation)
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