Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

Overview

SRDenseNet-pytorch

Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICCV_2017/papers/Tong_Image_Super-Resolution_Using_ICCV_2017_paper.pdf) image

Usage

Training

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

Pytorch SRDenseNet train

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
                        10 every n epochs, Default: n=30
  --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)

Test

usage: test.py [-h] [--cuda] [--model MODEL] [--imageset IMAGESET] [--scale SCALE]

Pytorch SRDenseNet Test

optional arguments:
  -h, --help     show this help message and exit
  --cuda         use cuda?
  --model MODEL  model path
  --imageset IMAGESET  imageset name
  --scale SCALE  scale factor, Default: 4

Prepare Training dataset

The training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.

Prepare Test dataset

The test imageset is generated with Matlab Bicubic Interplotation, please refer Code for test for creating test imageset.

Performance

We provide a pretrained .SRDenseNet x4 model trained on DIV2K images from [DIV2K_train_HR] (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip).While I use the SR_DenseNet to train this model, so the performance is test based on this code.

Non-overlapping sub-images with a size of 96 × 96 were cropped in the HR space. Other settings is the same as the original paper

  • Performance in PSNR on Set5, Set14, and BSD100
DataSet/Method Paper PyTorch
Set5 32.02/0.893 31.57/0.883
Set14 28.50/0.778 28.11/0.771
BSD100 27.53/0.733 27.32/0.729
A Human-in-the-Loop workflow for creating HD images from text

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Jina AI 2.5k Jan 02, 2023
Implementation of Shape and Electrostatic similarity metric in deepFMPO.

DeepFMPO v3D Code accompanying the paper "On the value of using 3D-shape and electrostatic similarities in deep generative methods". The paper can be

34 Nov 28, 2022
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
A cross-lingual COVID-19 fake news dataset

CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,

Yingtong Dou 11 Dec 01, 2022
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

OptiCL OptiCL is an end-to-end framework for mixed-integer optimization (MIO) with data-driven learned constraints. We address a problem setting in wh

Holly Wiberg 57 Dec 26, 2022
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi

Rakuten Group, Inc. 0 Nov 19, 2021
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited b

Facebook Research 61 Nov 28, 2022
Unrolled Generative Adversarial Networks

Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein arxiv:1611.02163 This repo contains an example notebo

Ben Poole 292 Dec 06, 2022
An self sufficient AI that crawls the web to learn how to generate art from keywords

Roxx-IO - The Smart Artist AI! TO DO / IDEAS Implement Web-Scraping Functionality Figure out a less annoying (and an off button for it) text to speech

Tatz 5 Mar 21, 2022
A Python wrapper for Google Tesseract

Python Tesseract Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded i

Matthias A Lee 4.6k Jan 05, 2023
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Jan 02, 2023
MRI reconstruction (e.g., QSM) using deep learning methods

deepMRI: Deep learning methods for MRI Authors: Yang Gao, Hongfu Sun This repo is devloped based on Pytorch (1.8 or later) and matlab (R2019a or later

Hongfu Sun 17 Dec 18, 2022
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Andreas Veit 176 Dec 07, 2022
✨风纪委员会自动投票脚本,利用Github Action帮你进行裁决操作(为了让其他风纪委员有案件可判,本程序从中午12点才开始运行,有需要请自己修改运行时间)

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能,喜欢请给我点个STAR吧! 如果你不是风纪委员,在符合风纪委员申请条件的情况下,本脚本会自动帮你申请 投票时间是早上八点,如果有需要请自行修改.github/workflows/Judge.yml中的时间,

Pesy Wu 25 Feb 17, 2021
Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020 BibTeX @INPROCEEDINGS{punnappurath2020modeling, author={Abhi

Abhijith Punnappurath 22 Oct 01, 2022
Multi-Joint dynamics with Contact. A general purpose physics simulator.

MuJoCo Physics MuJoCo stands for Multi-Joint dynamics with Contact. It is a general purpose physics engine that aims to facilitate research and develo

DeepMind 5.2k Jan 02, 2023
PyTorch implementation for "Mining Latent Structures with Contrastive Modality Fusion for Multimedia Recommendation"

MIRCO PyTorch implementation for paper: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation Dependencies Python 3.

Big Data and Multi-modal Computing Group, CRIPAC 9 Dec 08, 2022
A library for hidden semi-Markov models with explicit durations

hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for

Joris Vankerschaver 69 Dec 20, 2022
Predicting Event Memorability from Contextual Visual Semantics

Predicting Event Memorability from Contextual Visual Semantics

0 Oct 06, 2021
Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images This is the reference implementation of "Learning Generative Models of Texture

Dario Pavllo 115 Jan 07, 2023