(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

Related tags

Deep LearningClassSR
Overview

ClassSR

(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

Paper

Authors: Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong

Dependencies

Codes

  • Our codes version based on BasicSR.

How to test a single branch

  1. Clone this github repo.
git clone https://github.com/Xiangtaokong/ClassSR.git
cd ClassSR
  1. Download the testing datasets (DIV2K_valid).

  2. Download the divide_val.log and move it to .codes/data_scripts/.

  3. Generate simple, medium, hard (class1, class2, class3) validation data.

cd codes/data_scripts
python extract_subimages_test.py
python divide_subimages_test.py
  1. Download pretrained models and move them to ./experiments/pretrained_models/ folder.

  2. Run testing for a single branch.

cd codes
python test.py -opt options/test/test_FSRCNN.yml
python test.py -opt options/test/test_CARN.yml
python test.py -opt options/test/test_SRResNet.yml
python test.py -opt options/test/test_RCAN.yml
  1. The output results will be sorted in ./results.

How to test ClassSR

  1. Clone this github repo.
git clone https://github.com/Xiangtaokong/ClassSR.git
cd ClassSR
  1. Download the testing datasets (DIV8K). Test8K contains the images (index 1401-1500) from DIV8K. Test2K/4K contain the images (index 1201-1300/1301-1400) from DIV8K which are downsampled to 2K and 4K resolution.

  2. Download pretrained models and move them to ./experiments/pretrained_models/ folder.

  3. Run testing for ClassSR.

cd codes
python test_ClassSR.py -opt options/test/test_ClassSR_FSRCNN.yml
python test_ClassSR.py -opt options/test/test_ClassSR_CARN.yml
python test_ClassSR.py -opt options/test/test_ClassSR_SRResNet.yml
python test_ClassSR.py -opt options/test/test_ClassSR_RCAN.yml
  1. The output results will be sorted in ./results.

How to train a single branch

  1. Clone this github repo.
git clone https://github.com/Xiangtaokong/ClassSR.git
cd ClassSR
  1. Download the training datasets(DIV2K) and validation dataset(Set5).

  2. Download the divide_train.log and move it to .codes/data_scripts/.

  3. Generate simple, medium, hard (class1, class2, class3) training data.

cd codes/data_scripts
python data_augmentation.py
python extract_subimages_train.py
python divide_subimages_train.py
  1. Run training for a single branch (default branch1, the simplest branch).
cd codes
python train.py -opt options/train/train_FSRCNN.yml
python train.py -opt options/train/train_CARN.yml
python train.py -opt options/train/train_SRResNet.yml
python train.py -opt options/train/train_RCAN.yml
  1. The experiments will be sorted in ./experiments.

How to train ClassSR

  1. Clone this github repo.
git clone https://github.com/Xiangtaokong/ClassSR.git
cd ClassSR
  1. Download the training datasets (DIV2K) and validation dataset(DIV2K_valid, index 801-810).

  2. Generate training data (the all data(1.59M) in paper).

cd codes/data_scripts
python data_augmentation.py
python extract_subimages_ClassSR.py
  1. Download pretrained models(pretrained branches) and move them to ./experiments/pretrained_models/ folder.

  2. Run training for ClassSR.

cd codes
python train_ClassSR.py -opt options/train/train_ClassSR_FSRCNN.yml
python train_ClassSR.py -opt options/train/train_ClassSR_CARN.yml
python train_ClassSR.py -opt options/train/train_ClassSR_SRResNet.yml
python train_ClassSR.py -opt options/train/train_ClassSR_RCAN.yml
  1. The experiments will be sorted in ./experiments.

Contact

Email: [email protected]

Owner
Xiangtao Kong
Xiangtao Kong
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so

PGM-Lab 46 Nov 01, 2022
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Thomas Neumann 117 Nov 27, 2022
Mixup for Supervision, Semi- and Self-Supervision Learning Toolbox and Benchmark

OpenSelfSup News Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes). 'GaussianBlur' is

AI Lab, Westlake University 332 Jan 03, 2023
Code for the paper: Adversarial Machine Learning: Bayesian Perspectives

Code for the paper: Adversarial Machine Learning: Bayesian Perspectives This repository contains code for reproducing the experiments in the ** Advers

Roi Naveiro 2 Nov 11, 2022
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

564 Jan 02, 2023
This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks

NNProject - DeepMask This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks. Th

189 Nov 16, 2022
This is a computer vision based implementation of the popular childhood game 'Hand Cricket/Odd or Even' in python

Hand Cricket Table of Content Overview Installation Game rules Project Details Future scope Overview This is a computer vision based implementation of

Abhinav R Nayak 6 Jan 12, 2022
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals, CVPR2021

End-to-End Object Detection with Learnable Proposal, CVPR2021

Peize Sun 1.2k Dec 27, 2022
Segment axon and myelin from microscopy data using deep learning

Segment axon and myelin from microscopy data using deep learning. Written in Python. Using the TensorFlow framework. Based on a convolutional neural network architecture. Pixels are classified as eit

NeuroPoly 103 Nov 29, 2022
Adjust Decision Boundary for Class Imbalanced Learning

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

Peyton Byungju Kim 16 Jan 04, 2023
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Neurolab is a simple and powerful Neural Network Library for Python

Neurolab Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework

152 Dec 06, 2022
Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of bra

Angad Bajwa 8 Oct 27, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

28 Dec 02, 2022
Seg-Torch for Image Segmentation with Torch

Seg-Torch for Image Segmentation with Torch This work was sparked by my personal research on simple segmentation methods based on deep learning. It is

Eren Gölge 37 Dec 12, 2022
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis (CVPR2022)

Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis Multi-View Consistent Generative Adversarial Networks for 3D-aware

Xuanmeng Zhang 78 Dec 10, 2022
This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"

Learning to propose objects This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Ko

Philipp Krähenbühl 90 Sep 10, 2021
TUPÃ was developed to analyze electric field properties in molecular simulations

TUPÃ: Electric field analyses for molecular simulations What is TUPÃ? TUPÃ (pronounced as tu-pan) is a python algorithm that employs MDAnalysis engine

Marcelo D. Polêto 10 Jul 17, 2022
SAAVN - Sound Adversarial Audio-Visual Navigation,ICLR2022 (In PyTorch)

SAAVN SAAVN Code release for paper "Sound Adversarial Audio-Visual Navigation,IC

YinfengYu 10 Aug 30, 2022