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
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
The official implementation for ACL 2021 "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval".

Code for "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval" (ACL 2021, Long) This is the repository for baseline m

Akari Asai 25 Oct 30, 2022
Semi-supervised learning for object detection

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection STAC is a simple yet effective SSL framework for visual object

Google Research 348 Dec 25, 2022
git《Self-Attention Attribution: Interpreting Information Interactions Inside Transformer》(AAAI 2021) GitHub:

Self-Attention Attribution This repository contains the implementation for AAAI-2021 paper Self-Attention Attribution: Interpreting Information Intera

60 Dec 29, 2022
[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao. This codebase contains baseline of our paper Mini

Lihe Yang 66 Nov 29, 2022
Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

The Temporal Robustness of Stochastic Signals Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals" Case stud

0 Oct 28, 2021
Flexible-Modal Face Anti-Spoofing: A Benchmark

Flexible-Modal FAS This is the official repository of "Flexible-Modal Face Anti-

Zitong Yu 22 Nov 10, 2022
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

Overview | Tutorials | Examples | Installation | FAQ | How to Cite Welcome to ktrain News and Announcements 2020-11-08: ktrain v0.25.x is released and

Arun S. Maiya 1.1k Jan 02, 2023
Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Code for KSDAgg: a KSD aggregated goodness-of-fit test This GitHub repository contains the code for the reproducible experiments presented in our pape

Antonin Schrab 5 Dec 15, 2022
BBB streaming without Xorg and Pulseaudio and Chromium and other nonsense (heavily WIP)

BBB Streamer NG? Makes a conference like this... ...streamable like this! I also recorded a small video showing the basic features: https://www.youtub

Lukas Schauer 60 Oct 21, 2022
MINOS: Multimodal Indoor Simulator

MINOS Simulator MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environ

194 Dec 27, 2022
Deep Learning pipeline for motor-imagery classification.

BCI-ToolBox 1. Introduction BCI-ToolBox is deep learning pipeline for motor-imagery classification. This repo contains five models: ShallowConvNet, De

DongHee 18 Oct 31, 2022
MPViT:Multi-Path Vision Transformer for Dense Prediction

MPViT : Multi-Path Vision Transformer for Dense Prediction This repository inlcu

Youngwan Lee 272 Dec 20, 2022
Interpretation of T cell states using reference single-cell atlases

Interpretation of T cell states using reference single-cell atlases ProjecTILs is a computational method to project scRNA-seq data into reference sing

Cancer Systems Immunology Lab 139 Jan 03, 2023
Parameterising Simulated Annealing for the Travelling Salesman Problem

Parameterising Simulated Annealing for the Travelling Salesman Problem

Gary Sun 55 Jun 15, 2022
QHack—the quantum machine learning hackathon

Official repo for QHack—the quantum machine learning hackathon

Xanadu 72 Dec 21, 2022
Focal and Global Knowledge Distillation for Detectors

FGD Paper: Focal and Global Knowledge Distillation for Detectors Install MMDetection and MS COCO2017 Our codes are based on MMDetection. Please follow

Mesopotamia 261 Dec 23, 2022
Custom Implementation of Non-Deep Networks

ParNet Custom Implementation of Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Official Repository https

Pritama Kumar Nayak 20 May 27, 2022
[3DV 2021] A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks

dispersion-score Official implementation of 3DV 2021 Paper A Dataset-dispersion Perspective on Reconstruction versus Recognition in Single-view 3D Rec

Yefan 7 May 28, 2022
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022