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

A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

Heterogeneous Temporal Graph Neural Network

Accelerate Neural Net Training by Progressively Freezing Layers

HALO: A Skeleton-Driven Neural Occupancy Representation for Articulated Hands

Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Code for testing various M1 Chip benchmarks with TensorFlow.

A library for augmentation of a YOLO-formated dataset

Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis

code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

Multi-Stage Episodic Control for Strategic Exploration in Text Games

PyTorch DepthNet Training on Still Box dataset

Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

Official Pytorch implementation for "End2End Occluded Face Recognition by Masking Corrupted Features, TPAMI 2021"

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

Towards Debiasing NLU Models from Unknown Biases

Code for Talk-to-Edit (ICCV2021). Paper: Talk-to-Edit: Fine-Grained Facial Editing via Dialog.

Interactive Terraform visualization. State and configuration explorer.

This project hosts the code for implementing the ISAL algorithm for object detection and image classification

Implementation of Online Label Smoothing in PyTorch

CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks