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

Last update: Dec 12, 2022

Related tags

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

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

Related tags

Overview

DRRN-pytorch

Usage

Training

Evaluation

Prepare Training dataset

Performance

Owner

yun_yang

Shuffle Attention for MobileNetV3

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Syntax-Aware Action Targeting for Video Captioning

Implementation for the "Surface Reconstruction from 3D Line Segments" paper.

This repository provides an unified frameworks to train and test the state-of-the-art few-shot font generation (FFG) models.

Configure SRX interfaces with Scrapli

PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations" [arXiv 2022].

This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Deep Learning (with PyTorch)

Generate image analogies using neural matching and blending

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

An atmospheric growth and evolution model based on the EVo degassing model and FastChem 2.0

Yas CRNN model training - Yet Another Genshin Impact Scanner

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies

FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS

ICRA 2021 - Robust Place Recognition using an Imaging Lidar

Face recognize and crop them

Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.