AdaDM: Enabling Normalization for Image Super-Resolution

Related tags

Deep LearningAdaDM
Overview

AdaDM

AdaDM: Enabling Normalization for Image Super-Resolution.

You can apply BN, LN or GN in SR networks with our AdaDM. Pretrained models (EDSR*/RDN*/NLSN*) can be downloaded from Google Drive or BaiduYun. The password for BaiduYun is kymj.

📢 If you use BasicSR framework, you need to turn off the Exponential Moving Average (EMA) option when applying BN in the generator network (e.g., RRDBNet). You can disable EMA by setting ema_decay=0 in corresponding .yml configuration file.

Model Scale File name (.pt) Urban100 Manga109
EDSR 2 32.93 39.10
3 28.80 34.17
4 26.64 31.02
EDSR* 2 EDSR_AdaDM_DIV2K_X2 33.12 39.31
3 EDSR_AdaDM_DIV2K_X3 29.02 34.48
4 EDSR_AdaDM_DIV2K_X4 26.83 31.24
RDN 2 32.89 39.18
3 28.80 34.13
4 26.61 31.00
RDN* 2 RDN_AdaDM_DIV2K_X2 33.03 39.18
3 RDN_AdaDM_DIV2K_X3 28.95 34.29
4 RDN_AdaDM_DIV2K_X4 26.72 31.18
NLSN 2 33.42 39.59
3 29.25 34.57
4 26.96 31.27
NLSN* 2 NLSN_AdaDM_DIV2K_X2 33.59 39.67
3 NLSN_AdaDM_DIV2K_X3 29.53 34.95
4 NLSN_AdaDM_DIV2K_X4 27.24 31.73

Preparation

Please refer to EDSR for instructions on dataset download and software installation, then clone our repository as follows:

git clone https://github.com/njulj/AdaDM.git

Training

cd AdaDM/src
bash train.sh

Example training command in train.sh looks like:

CUDA_VISIBLE_DEVICES=$GPU_ID python3 main.py --template EDSR_paper --scale 2\
        --n_GPUs 1 --batch_size 16 --patch_size 96 --rgb_range 255 --res_scale 0.1\
        --save EDSR_AdaDM_Test_DIV2K_X2 --dir_data ../dataset --data_test Urban100\
        --epochs 1000 --decay 200-400-600-800 --lr 1e-4 --save_models --save_results 

Here, $GPU_ID specifies the GPU id used for training. EDSR_AdaDM_Test_DIV2K_X2 is the directory where all files are saved during training. --dir_data specifies the root directory for all datasets, you should place the DIV2K and benchmark (e.g., Urban100) datasets under this directory.

Testing

cd AdaDM/src
bash test.sh

Example testing command in test.sh looks like:

CUDA_VISIBLE_DEVICES=$GPU_ID python3 main.py --template EDSR_paper --scale $SCALE\
        --pre_train ../experiment/test/model/EDSR_AdaDM_DIV2K_X$SCALE.pt\
        --dir_data ../dataset --n_GPUs 1 --test_only --data_test $TEST_DATASET

Here, $GPU_ID specifies the GPU id used for testing. $SCALE indicates the upscaling factor (e.g., 2, 3, 4). --pre_train specifies the path of saved checkpoints. $TEST_DATASET indicates the dataset to be tested.

Acknowledgement

This repository is built on EDSR and NLSN. We thank the authors for sharing their codes.

Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources.

Illumination_Decomposition Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources. This code implements the

QAY 7 Nov 15, 2020
Memory-Augmented Model Predictive Control

Memory-Augmented Model Predictive Control This repository hosts the source code for the journal article "Composing MPC with LQR and Neural Networks fo

Fangyu Wu 1 Jun 19, 2022
ICCV2021 - A New Journey from SDRTV to HDRTV.

ICCV2021 - A New Journey from SDRTV to HDRTV.

XyChen 82 Dec 27, 2022
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 08, 2023
Hierarchical Attentive Recurrent Tracking

Hierarchical Attentive Recurrent Tracking This is an official Tensorflow implementation of single object tracking in videos by using hierarchical atte

Adam Kosiorek 147 Aug 07, 2021
[CVPR 2021] Teachers Do More Than Teach: Compressing Image-to-Image Models (CAT)

CAT arXiv Pytorch implementation of our method for compressing image-to-image models. Teachers Do More Than Teach: Compressing Image-to-Image Models Q

Snap Research 160 Dec 09, 2022
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Zhensu Sun 1 Oct 26, 2021
🐥A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

PyTorch implementation of OpenAI's Finetuned Transformer Language Model This is a PyTorch implementation of the TensorFlow code provided with OpenAI's

Hugging Face 1.4k Jan 05, 2023
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the

34 May 24, 2022
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchm

Filip Radenovic 188 Dec 17, 2022
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

RTFM This repo contains the Pytorch implementation of our paper: Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Lear

Yu Tian 242 Jan 08, 2023
Simulating an AI playing 2048 using the Expectimax algorithm

2048-expectimax Simulating an AI playing 2048 using the Expectimax algorithm The base game engine uses code from here. The AI player is modeled as a m

Subha Ramesh 2 Jan 31, 2022
A curated list of awesome Deep Learning tutorials, projects and communities.

Awesome Deep Learning Table of Contents Books Courses Videos and Lectures Papers Tutorials Researchers Websites Datasets Conferences Frameworks Tools

Christos 20k Jan 05, 2023
Its a Plant Leaf Disease Detection System based on Machine Learning.

My_Project_Code Its a Plant Leaf Disease Detection System based on Machine Learning. I have used Tomato Leaves Dataset from kaggle. This system detect

Sanskriti Sidola 3 Jun 15, 2022
Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

1 Jan 16, 2022
Differential fuzzing for the masses!

NEZHA NEZHA is an efficient and domain-independent differential fuzzer developed at Columbia University. NEZHA exploits the behavioral asymmetries bet

147 Dec 05, 2022
https://sites.google.com/cornell.edu/recsys2021tutorial

Counterfactual Learning and Evaluation for Recommender Systems (RecSys'21 Tutorial) Materials for "Counterfactual Learning and Evaluation for Recommen

yuta-saito 45 Nov 10, 2022
Neural style in TensorFlow! 🎨

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Anish Athalye 5.5k Dec 29, 2022
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Katherine Crowson 53 Dec 29, 2022