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

《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression problems

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

Code for visualizing the loss landscape of neural nets

Inflated i3d network with inception backbone, weights transfered from tensorflow

Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

Consistency Regularization for Adversarial Robustness

Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks

Exploring whether attention is necessary for vision transformers

Repository of best practices for deep learning in Julia, inspired by fastai

Combinatorial model of ligand-receptor binding

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

MakeItTalk: Speaker-Aware Talking-Head Animation

Resources for the Ki testnet challenge

This repo is about implementing different approaches of pose estimation and also is a sub-task of the smart hospital bed project :smile:

Decision Transformer: A brand new Offline RL Pattern

Python package for multiple object tracking research with focus on laboratory animals tracking.

The code of Zero-shot learning for low-light image enhancement based on dual iteration

Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation