{"repo":"aiff22/DPED","free":true,"listed":false,"github":"https://github.com/aiff22/DPED","clone":"git clone https://github.com/aiff22/DPED.git","description":"Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks","language":"Python","stars":1710,"topics":["image-enhancement","image-processing","computer-vision","deep-learning","dped","gan","convolutional-neural-networks","generative-adversarial-networks"],"license":null,"category":"machine-learning","readme_excerpt":"DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks 1. Overview [[Paper]](https://arxiv.org/pdf/1704.02470.pdf) [[Project webpage]](https://aiff22.github.io/) [[Enhancing RAW photos]](https://github.com/aiff22/PyNET) [[Rendering Bokeh Effect]](https://github.com/aiff22/PyNET-Bokeh) The provided code implements the paper that presents an end-to-end deep learning approach for translating ordinary photos from smartphones into DSLR-quality images. The learned model can be applied to photos of arbitrary resolution, while the methodology itself is generalized to any type of digital camera. More visual results can be found here. 2. Prerequisites - Python + Pillow, scipy, numpy, imageio packages - TensorFlow 1.x / 2.x + CUDA CuDNN - Nvidia GPU 3. First steps - Download the pre-trained VGG-19 model and put it into vgg pretrained/ folder - Download DPED dataset (patches for CNN training) and extract it into dped/ folder. This folder should contain three subolders: sony/ , iphone/ and blackberry/ 4. Train the model Obligatory parameters: : , or Optional parameters and their default values: :   -   batch size [smaller values can lead to unstable training] :   -   the number of training patches randomly loaded each iterations :   -   each iterations the model is saved and the training data is reloaded :   -   the number of training iterations :   -   learning rate :   -   the weight of the content loss :  ","default_branch":null,"files":null,"tree":[],"storefront":"/r/aiff22","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aiff22/DPED/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}