Pytorch implementation of Deep Recursive Residual Network for Super Resolution (DRRN)

Overview

DRRN-pytorch

This is an unofficial implementation of "Deep Recursive Residual Network for Super Resolution (DRRN)", CVPR 2017 in Pytorch. [Paper]

You can get the official Caffe implementation here.

Usage

Training

usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
               [--step STEP] [--cuda] [--resume RESUME]
               [--start-epoch START_EPOCH] [--clip CLIP] [--threads THREADS]
               [--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
               [--pretrained PRETRAINED]
               
optional arguments:
  -h, --help            Show this help message and exit
  --batchSize           Training batch size
  --nEpochs             Number of epochs to train for
  --lr                  Learning rate. Default=0.1
  --step                Learning rate decay, Default: n=5 epochs
  --cuda                Use cuda?
  --resume              Path to checkpoint
  --clip                Clipping Gradients. Default=0.01
  --threads             Number of threads for data loader to use Default=1
  --momentum            Momentum, Default: 0.9
  --weight-decay        Weight decay, Default: 1e-4
  --pretrained          Path to the pretrained model, used for weight initialization (default: none)

Evaluation

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

PyTorch DRRN Evaluation

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5

An example of training usage is shown as follows:

python eval.py --cuda

Prepare Training dataset

  • the training data is generated with Matlab Bicubic Interpolation, please refer Code for Data Generation for creating training files.

Performance

  • We provide a rough pre-trained DRRN_B1U25 model trained on 291 images with data augmentation. The model can achieve a better performance with a smart optimization strategy. For the DRRN_B1U9 implementation, you can manually modify the number of recursive blocks here.
  • The same adjustable gradient clipping's implementation as original paper.
  • No bias is used in this implementation.
  • No batch normalization is used in this implementation.
  • Performance in PSNR on Set5
Scale DRRN_B1U25 Paper DRRN_B1U25 PyTorch
x2 37.74 37.69
x3 34.03 34.02
x4 31.68 31.70
Owner
yun_yang
yun_yang
Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

Yandex 662 Nov 20, 2022
Implementation of "RaScaNet: Learning Tiny Models by Raster-Scanning Image" from CVPR 2021.

RaScaNet: Learning Tiny Models by Raster-Scanning Images Deploying deep convolutional neural networks on ultra-low power systems is challenging, becau

SAIT (Samsung Advanced Institute of Technology) 5 Dec 26, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022
Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

PubTables-1M This repository contains training and evaluation code for the paper "PubTables-1M: Towards a universal dataset and metrics for training a

Microsoft 365 Jan 04, 2023
CAUSE: Causality from AttribUtions on Sequence of Events

CAUSE: Causality from AttribUtions on Sequence of Events

Wei Zhang 21 Dec 01, 2022
a general-purpose Transformer based vision backbone

Swin Transformer By Ze Liu*, Yutong Lin*, Yue Cao*, Han Hu*, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo. This repo is the official implement

Microsoft 9.9k Jan 08, 2023
IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL.

IJON SPACE EXPLORER IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL. Using only a small (usually one line) annotati

Chair for Sys­tems Se­cu­ri­ty 146 Dec 16, 2022
Implementation of the paper "Shapley Explanation Networks"

Shapley Explanation Networks Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimenta

68 Dec 27, 2022
This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.

ERFNet This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation. NEW!! New PyTorch

Edu 104 Jan 05, 2023
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

47 Dec 28, 2022
PyTorch META-DATASET (Few-shot classification benchmark)

PyTorch META-DATASET (Few-shot classification benchmark) This repo contains a PyTorch implementation of meta-dataset and a unified implementation of s

Malik Boudiaf 39 Oct 31, 2022
JittorVis - Visual understanding of deep learning models

JittorVis: Visual understanding of deep learning model JittorVis is an open-source library for understanding the inner workings of Jittor models by vi

thu-vis 182 Jan 06, 2023
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

Jason Kuen 17 Jul 04, 2022
Here we present the implementation in TensorFlow of our work about liver lesion segmentation accepted in the Machine Learning 4 Health Workshop

Detection-aided liver lesion segmentation Here we present the implementation in TensorFlow of our work about liver lesion segmentation accepted in the

Image Processing Group - BarcelonaTECH - UPC 96 Oct 26, 2022
Multiview 3D object detection on MultiviewC dataset through moft3d.

Voxelized 3D Feature Aggregation for Multiview Detection [arXiv] Multiview 3D object detection on MultiviewC dataset through VFA. Introduction We prop

Jiahao Ma 20 Dec 21, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
A copy of Ares that costs 30 fucking dollars.

Finalement, j'ai décidé d'abandonner cette idée, je me suis comporté comme un enfant qui été en colère. Comme m'ont dit certaines personnes j'ai des c

Bleu 24 Apr 14, 2022
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan NOTE: This documentation describes a BETA release of PyStan 3. PyStan is a Python interface to Stan, a package for Bayesian inference. Stan® is

Stan 229 Dec 29, 2022
DeepStochlog Package For Python

DeepStochLog Installation Installing SWI Prolog DeepStochLog requires SWI Prolog to run. Run the following commands to install: sudo apt-add-repositor

KU Leuven Machine Learning Research Group 17 Dec 23, 2022
Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed

fast-Bart Reduction of BART model size by 3X, and boost in inference speed up to 3X BART implementation of the fastT5 library (https://github.com/Ki6a

Siddharth Sharma 19 Dec 09, 2022