PyTorch code for our paper "Gated Multiple Feedback Network for Image Super-Resolution" (BMVC2019)

Related tags

Deep LearningGMFN
Overview

Gated Multiple Feedback Network for Image Super-Resolution

This repository contains the PyTorch implementation for the proposed GMFN [arXiv].

The framework of our proposed GMFN. The colored arrows among different time steps denote the multiple feedback connections. The high-level information carried by them helps low-level features become more representative.

Demo

Clone SRFBN as the backbone and satisfy its requirements.

Test

  1. Copy ./networks/gmfn_arch.py into SRFBN_CVPR19/networks/

  2. Download the pre-trained models from Google driver or Baidu Netdisk, unzip and place them into SRFBN_CVPR19/models.

  3. Copy ./options/test/ to SRFBN_CVPR19/options/test/.

  4. Run commands cd SRFBN_CVPR19 and one of followings for evaluation on Set5:

python test.py -opt options/test/test_GMFN_x2.json
python test.py -opt options/test/test_GMFN_x3.json
python test.py -opt options/test/test_GMFN_x4.json
  1. Finally, PSNR/SSIM values for Set5 are shown on your screen, you can find the reconstruction images in ./results.

To test GMFN on other standard SR benchmarks or your own images, please refer to the instruction in SRFBN.

Train

  1. Prepare the training set according to this (1-3).
  2. Modify ./options/train/train_GMFN.json by following the instructions in ./options/train/README.md.
  3. Run commands:
cd SRFBN_CVPR19
python train.py -opt options/train/train_GNFN.json

  1. You can monitor the training process in ./experiments.

  2. Finally, you can follow the test pipeline to evaluate the model trained by yourself.

Performance

Quantitative Results

Quantitative evaluation under scale factors x2, x3 and x4. The best performance is shown in bold and the second best performance is underlined.

More Qualitative Results (x4)

Acknowledgment

If you find our work useful in your research or publications, please consider citing:

@inproceedings{li2019gmfn,
    author = {Li, Qilei and Li, Zhen and Lu, Lu and Jeon, Gwanggil and Liu, Kai and Yang, Xiaomin},
    title = {Gated Multiple Feedback Network for Image Super-Resolution},
    booktitle = {The British Machine Vision Conference (BMVC)},
    year = {2019}
}

@inproceedings{li2019srfbn,
    author = {Li, Zhen and Yang, Jinglei and Liu, Zheng and Yang, Xiaomin and Jeon, Gwanggil and Wu, Wei},
    title = {Feedback Network for Image Super-Resolution},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year= {2019}
}
You might also like...
Pytorch implementation of our paper under review — Lottery Jackpots Exist in Pre-trained Models

Lottery Jackpots Exist in Pre-trained Models (Paper Link) Requirements Python = 3.7.4 Pytorch = 1.6.1 Torchvision = 0.4.1 Reproduce the Experiment

Yuxin Zhang 27 Jun 28, 2022
The repository offers the official implementation of our paper in PyTorch.

Cloth Interactive Transformer (CIT) Cloth Interactive Transformer for Virtual Try-On Bin Ren1, Hao Tang1, Fanyang Meng2, Runwei Ding3, Ling Shao4, Phi

Bingoren 49 Dec 1, 2022
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.

st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha

Diplodocus 258 Jan 2, 2023
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
Pytorch implementation for our ICCV 2021 paper
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
This is the official pytorch implementation for our ICCV 2021 paper
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

🌈 ERASOR (RA-L'21 with ICRA Option) Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point C

Hyungtae Lim 225 Dec 29, 2022
PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.
PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

Saim Wani 4 May 8, 2022
PyTorch implementation for our NeurIPS 2021 Spotlight paper
PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".

Long Short-Term Transformer for Online Action Detection Introduction This is a PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short

null 77 Dec 16, 2022
Official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels".

WarPI The official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels". Run python main.py --corruption_type

Haoliang Sun 3 Sep 3, 2022
Comments
  • Approximately how many epoches will reach the results in the paper (4x SR result)

    Approximately how many epoches will reach the results in the paper (4x SR result)

    Hi, liqilei After I have run about 700 epoches, the reult on val set is 32.41(highest result). I want to know if my training process seems to be problematic? How long did you reach 32.47 of SRFBN when you were training? How long does it take to reach 32.70? Thank you.

    opened by Senwang98 7
  • train error size not match

    train error size not match

    CUDA_VISIBLE_DEVICES=0 python train.py -opt options/train/train_GMFN.json I use celeba dataset train

    ===> Training Epoch: [1/1000]... Learning Rate: 0.000200 Epoch: [1/1000]: 0%| | 0/251718 [00:00<?, ?it/s] Traceback (most recent call last): File "train.py", line 131, in main() File "train.py", line 69, in main iter_loss = solver.train_step() File "/exp_sr/SRFBN/solvers/SRSolver.py", line 104, in train_step loss_steps = [self.criterion_pix(sr, split_HR) for sr in outputs] File "/exp_sr/SRFBN/solvers/SRSolver.py", line 104, in loss_steps = [self.criterion_pix(sr, split_HR) for sr in outputs] File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call result = self.forward(*input, **kwargs) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/modules/loss.py", line 87, in forward return F.l1_loss(input, target, reduction=self.reduction) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/functional.py", line 1702, in l1_loss input, target, reduction) File "/toolscnn/env_pyt0.4_py3.5_awsrn/lib/python3.5/site-packages/torch/nn/functional.py", line 1674, in _pointwise_loss return lambd_optimized(input, target, reduction) RuntimeError: input and target shapes do not match: input [16 x 3 x 192 x 192], target [16 x 3 x 48 x 48] at /pytorch/aten/src/THCUNN/generic/AbsCriterion.cu:12

    opened by yja1 3
  • Not an Issue

    Not an Issue

    Hey @Paper99,

    Thanks for sharing your code! I wonder if it is possible to help with visualizing featuer-maps as you did in your paper figure 4.

    Thanks

    opened by Auth0rM0rgan 1
  • My training result with scale = 2

    My training result with scale = 2

    Hi, After I have trained the DIV2k, I get the final result(use best_ckp.pth to test):

    set5:38.16/0.9610
set14:33.91/0.9203
urban100:32.81/0.9349
B100:32.30/0.9011
manga109:39.01/0.9776

    It seems much lower than that in your paper.

    opened by Senwang98 6
Owner
Qilei Li
Qilei Li
GitHub Repository
Split Variational AutoEncoder

Split-VAE Split Variational AutoEncoder Introduction This repository contains and implemementation of a Split Variational AutoEncoder (SVAE). In a SVA

Andrea Asperti 2 Sep 02, 2022
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022
基于YoloX目标检测+DeepSort算法实现多目标追踪Baseline

项目简介： 使用YOLOX+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。 代码地址（欢迎star）： https://github.com/Sharpiless/yolox-deepsort/ 最终效果： 运行demo： python demo

114 Dec 30, 2022
Imagededup - 😎 Finding duplicate images made easy

imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.

idealo 4.3k Jan 07, 2023
Have you ever wondered how cool it would be to have your own A.I

Have you ever wondered how cool it would be to have your own A.I. assistant Imagine how easier it would be to send emails without typing a single word, doing Wikipedia searches without opening web br

Harsh Gupta 1 Nov 09, 2021
Deep Learning Head Pose Estimation using PyTorch.

Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.

Nataniel Ruiz 1.3k Dec 26, 2022
[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects YouTube | arXiv Prerequisites Kaolin is available here:

Denys Rozumnyi 107 Dec 26, 2022
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

79 Jan 06, 2023
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 27, 2022
Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation

TimeLens: Event-based Video Frame Interpolation This repository is about the High Speed Event and RGB (HS-ERGB) dataset, used in the 2021 CVPR paper T

Robotics and Perception Group 544 Dec 19, 2022
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022
Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

Seonggwan Ko 9 Jul 30, 2022
Agile SVG maker for python

Agile SVG Maker Need to draw hundreds of frames for a GIF? Need to change the style of all pictures in a PPT? Need to draw similar images with differe

SemiWaker 4 Sep 25, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
This repository contains an implementation of ConvMixer for the ICLR 2022 submission "Patches Are All You Need?".

Patches Are All You Need? 🤷 This repository contains an implementation of ConvMixer for the ICLR 2022 submission "Patches Are All You Need?". Code ov

ICLR 2022 Author 934 Dec 30, 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
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

536 Dec 20, 2022
NeWT: Natural World Tasks

NeWT: Natural World Tasks This repository contains resources for working with the NeWT dataset. ❗ At this time the binary tasks are not publicly avail

Visipedia 26 Oct 18, 2022
Rethinking the U-Net architecture for multimodal biomedical image segmentation

MultiResUNet Rethinking the U-Net architecture for multimodal biomedical image segmentation This repository contains the original implementation of "M

Nabil Ibtehaz 308 Jan 05, 2023