pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802

Related tags

Deep Learningresolutionpytorchpytorch-srresnet
Overview

PyTorch SRResNet

Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs/1609.04802) in PyTorch

Usage

Training

usage: main_srresnet.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS]
                        [--lr LR] [--step STEP] [--cuda] [--resume RESUME]
                        [--start-epoch START_EPOCH] [--threads THREADS]
                        [--pretrained PRETRAINED] [--vgg_loss] [--gpus GPUS]

optional arguments:
  -h, --help            show this help message and exit
  --batchSize BATCHSIZE
                        training batch size
  --nEpochs NEPOCHS     number of epochs to train for
  --lr LR               Learning Rate. Default=1e-4
  --step STEP           Sets the learning rate to the initial LR decayed by
                        momentum every n epochs, Default: n=500
  --cuda                Use cuda?
  --resume RESUME       Path to checkpoint (default: none)
  --start-epoch START_EPOCH
                        Manual epoch number (useful on restarts)
  --threads THREADS     Number of threads for data loader to use, Default: 1
  --pretrained PRETRAINED
                        path to pretrained model (default: none)
  --vgg_loss            Use content loss?
  --gpus GPUS           gpu ids (default: 0)

An example of training usage is shown as follows:

python main_srresnet.py --cuda --vgg_loss --gpus 0

demo

usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE]
               [--dataset DATASET] [--scale SCALE] [--gpus GPUS]

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --image IMAGE      image name
  --dataset DATASET  dataset name
  --scale SCALE      scale factor, Default: 4
  --gpus GPUS        gpu ids (default: 0)

We convert Set5 test set images to mat format using Matlab, for simple image reading An example of usage is shown as follows:

python demo.py --model model/model_srresnet.pth --dataset Set5 --image butterfly_GT --scale 4 --cuda

Eval

usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
               [--scale SCALE] [--gpus GPUS]

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5
  --scale SCALE      scale factor, Default: 4
  --gpus GPUS        gpu ids (default: 0)

We convert Set5 test set images to mat format using Matlab. Since PSNR is evaluated on only Y channel, we import matlab in python, and use rgb2ycbcr function for converting rgb image to ycbcr image. You will have to setup the matlab python interface so as to import matlab library. An example of usage is shown as follows:

python eval.py --model model/model_srresnet.pth --dataset Set5 --cuda

Prepare Training dataset

  • Please refer Code for Data Generation for creating training files.
  • Data augmentations including flipping, rotation, downsizing are adopted.

Performance

  • We provide a pretrained model trained on 291 images with data augmentation
  • Instance Normalization is applied instead of Batch Normalization for better performance
  • So far performance in PSNR is not as good as paper, any suggestion is welcome
Dataset SRResNet Paper SRResNet PyTorch
Set5 32.05 31.80
Set14 28.49 28.25
BSD100 27.58 27.51

Result

From left to right are ground truth, bicubic and SRResNet

Owner
Jiu XU
Computer Vision Engineering Manager @ Apple
Jiu XU
GitHub Repository
This is a clean and robust Pytorch implementation of DQN and Double DQN.

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

XinJingHao 15 Dec 27, 2022
Apply our monocular depth boosting to your own network!

MergeNet - Boost Your Own Depth Boost custom or edited monocular depth maps using MergeNet Input Original result After manual editing of base You can

Computational Photography Lab @ SFU 142 Dec 17, 2022
Versatile Generative Language Model

Versatile Generative Language Model This is the implementation of the paper: Exploring Versatile Generative Language Model Via Parameter-Efficient Tra

Zhaojiang Lin 17 Dec 02, 2022
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

1.4k Jan 04, 2023
Video2x - A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR.

Official Discussion Group (Telegram): https://t.me/video2x A Discord server is also available. Please note that most developers are only on Telegram.

K4YT3X 5.9k Dec 31, 2022
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision Project | PDF | Poster Fangyu Li, N. Dinesh Reddy, X

25 Dec 21, 2022
DeepAL: Deep Active Learning in Python

DeepAL: Deep Active Learning in Python Python implementations of the following active learning algorithms: Random Sampling Least Confidence [1] Margin

Kuan-Hao Huang 583 Jan 03, 2023
Multiview 3D object detection on MultiviewC dataset through moft3d.

Multiview Orthographic Feature Transformation for 3D Object Detection Multiview 3D object detection on MultiviewC dataset through moft3d. Introduction

Jiahao Ma 20 Dec 21, 2022
Code for Learning to Segment The Tail (LST)

Learning to Segment the Tail [arXiv] In this repository, we release code for Learning to Segment The Tail (LST). The code is directly modified from th

47 Nov 07, 2022
An open-source, low-cost, image-based weed detection device for fallow scenarios.

Welcome to the OpenWeedLocator (OWL) project, an opensource hardware and software green-on-brown weed detector that uses entirely off-the-shelf compon

Guy Coleman 145 Jan 05, 2023
Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

Huaijun Jiang 3 Nov 01, 2022
A custom DeepStack model that has been trained detecting ONLY the USPS logo

This repository provides a custom DeepStack model that has been trained detecting ONLY the USPS logo. This was created after I discovered that the Deepstack OpenLogo custom model I was using did not

Stephen Stratoti 9 Dec 27, 2022
An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022

Dual Correlation Reduction Network An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022. Any

yueliu1999 109 Dec 23, 2022
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Molecular Docking integrated in Jupyter Notebooks Description | Citation | Installation | Examples | Limitations | License Table of content Descriptio

Angel J. Ruiz Moreno 173 Dec 25, 2022
Boundary-aware Transformers for Skin Lesion Segmentation

Boundary-aware Transformers for Skin Lesion Segmentation Introduction This is an official release of the paper Boundary-aware Transformers for Skin Le

Jiacheng Wang 79 Dec 16, 2022
The Video-based Accident Detection System built in Python

Accident-detection-system About the Project This Repository contains the Video-based Accident Detection System built in Python. Contributors Yukta Gop

SURYAVANSHI SNEHAL BALKRISHNA 50 Dec 07, 2022
CondNet: Conditional Classifier for Scene Segmentation

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

ycszen 31 Jul 22, 2022
Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module

Invariant Point Attention - Pytorch Implementation of Invariant Point Attention as a standalone module, which was used in the structure module of Alph

Phil Wang 113 Jan 05, 2023
The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

AICITY2021_Track2_DMT The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop. Introduction

Hao Luo 91 Dec 21, 2022
This is the official code release for the paper Shape and Material Capture at Home

This is the official code release for the paper Shape and Material Capture at Home. The code enables you to reconstruct a 3D mesh and Cook-Torrance BRDF from one or more images captured with a flashl

89 Dec 10, 2022