Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Related tags

Deep LearningYTMT-Strategy
Overview

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021)

by Qiming Hu, Xiaojie Guo.

Dependencies

  • Python3
  • PyTorch>=1.0
  • OpenCV-Python, TensorboardX, Visdom
  • NVIDIA GPU+CUDA

Network Architecture

figure_arch

🚀 1. Single Image Reflection Separation

Data Preparation

Training dataset

  • 7,643 images from the Pascal VOC dataset, center-cropped as 224 x 224 slices to synthesize training pairs.
  • 90 real-world training pairs provided by Zhang et al.

Tesing dataset

  • 45 real-world testing images from CEILNet dataset.
  • 20 real testing pairs provided by Zhang et al.
  • 454 real testing pairs from SIR^2 dataset, containing three subsets (i.e., Objects (200), Postcard (199), Wild (55)).

Usage

Training

  • For stage 1: python train_sirs.py --inet ytmt_ucs --model ytmt_model_sirs --name ytmt_ucs_sirs --hyper --if_align
  • For stage 2: python train_twostage_sirs.py --inet ytmt_ucs --model twostage_ytmt_model --name ytmt_uct_sirs --hyper --if_align --resume --resume_epoch xx --checkpoints_dir xxx

Testing

python test_sirs.py --inet ytmt_ucs --model twostage_ytmt_model --name ytmt_uct_sirs_test --hyper --if_align --resume --icnn_path ./checkpoints/ytmt_uct_sirs/twostage_unet_68_077_00595364.pt

Trained weights

Google Drive

Visual comparison on real20 and SIR^2

figure_eval

Visual comparison on real45

figure_test

🚀 2. Single Image Denoising

Data Preparation

Training datasets

400 images from the Berkeley segmentation dataset, following DnCNN.

Tesing datasets

BSD68 dataset and Set12.

Usage

Training

python train_denoising.py --inet ytmt_pas --name ytmt_pas_denoising --preprocess True --num_of_layers 9 --mode B --preprocess True

Testing

python test_denoising.py --inet ytmt_pas --name ytmt_pas_denoising_blindtest_25 --test_noiseL 25 --num_of_layers 9 --test_data Set68 --icnn_path ./checkpoints/ytmt_pas_denoising_49_157500.pt

Trained weights

Google Drive

Visual comparison on a sample from BSD68

figure_eval_denoising

🚀 3. Single Image Demoireing

Data Preparation

Training dataset

AIM 2019 Demoireing Challenge

Tesing dataset

100 moireing and clean pairs from AIM 2019 Demoireing Challenge.

Usage

Training

python train_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire --hyper --if_align

Testing

python test_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire_test --hyper --if_align --resume --icnn_path ./checkpoints/ytmt_ucs_demoire/ytmt_ucs_opt_086_00860000.pt

Trained weights

Google Drive

Visual comparison on the validation set of LCDMoire

figure_eval_demoire

You might also like...
Image-to-Image Translation with Conditional Adversarial Networks (Pix2pix) implementation in keras

pix2pix-keras Pix2pix implementation in keras. Original paper: Image-to-Image Translation with Conditional Adversarial Networks (pix2pix) Paper Author

William Falcon 141 Dec 30, 2022
Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

This is a Python implementation of cover trees, a data structure for finding nearest neighbors in a general metric space (e.g., a 3D box with periodic

Patrick Varilly 28 Nov 25, 2022
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

RGF-team 364 Dec 28, 2022
Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

xRBM Library Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow Installation Using pip: pip install xrbm Examples Tut

Omid Alemi 55 Dec 29, 2022
A fast Evolution Strategy implementation in Python

Evostra: Evolution Strategy for Python Evolution Strategy (ES) is an optimization technique based on ideas of adaptation and evolution. You can learn

Mika 251 Dec 8, 2022
🌳 A Python-inspired implementation of the Optimum-Path Forest classifier.

OPFython: A Python-Inspired Optimum-Path Forest Classifier Welcome to OPFython. Note that this implementation relies purely on the standard LibOPF. Th

Gustavo Rosa 30 Jan 4, 2023
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Geometric Vector Perceptron Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biom

Phil Wang 59 Nov 24, 2022
Official implementation of AAAI-21 paper
Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

Description: This is the official implementation of our AAAI-21 accepted paper Label Confusion Learning to Enhance Text Classification Models. The str

null 101 Nov 25, 2022
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Official PyTorch implementation for paper Context Matters: Gra

null 49 Nov 23, 2022
Comments
  • Datasets

    Datasets

    Hi,

    I have been trying to experiment with the model but I'm having trouble finding the correct datasets for testing. The Sirs2 dataset in the provided link doesn't have the images set up with the naming conventions used in the script. Could you please direct me to the correct data sets for testing and training? Is there a separate repository that you have used?

    Thanks so much,

    David

    opened by davidgaddie 3
  • About Training Details

    About Training Details

    Hello, thank you for sharing your wonderful work. I have some question about the triaining details. It says the training epoch is 120 in your paper but the epoch is set to 60 in YTMT-Strategy/options/net_options/train_options.py. Moreover, the best model in your paper is YTMT-UCT which need two stages training. Can you provide the training settings of the YTMT-UCT (epoch, batchsize...)? Look forward to your reply!

    opened by DUT-CSJ 2
  • CUDA vram allocation issue

    CUDA vram allocation issue

    Hi,

    I've been trying to run the reflection test code, but I get this error: RuntimeError: CUDA out of memory. Tried to allocate 15.66 GiB (GPU 0; 22.20 GiB total capacity; 16.09 GiB already allocated; 2.68 GiB free; 17.55 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

    I'm running on an A10G GPU on AWS. I suspect that maybe the dataset is incorrect as each image in the dataset I have is around 800MB. If that's the case can I please be directed to the correct repository for the read20_420 images?

    Thanks so much,

    David

    opened by davidgaddie 1
  • test demoire error

    test demoire error

    Thanks for your great work ,but some error when I run: python test_demoire.py --inet ytmt_ucs --model ytmt_model_demoire --name ytmt_uas_demoire_test --hyper --if_align --resume --icnn_path checkpoints/ytmt_ucs_demoire/ytmt_ucs_demoire_opt_086_00860000.pt

    -------------- End ---------------- [i] initialization method [edsr] Traceback (most recent call last): File "test_demoire.py", line 28, in engine = Engine(opt) File "/nfs_data/code/YTMT-Strategy-main/engine.py", line 19, in init self.__setup() File "/nfs_data/code/YTMT-Strategy-main/engine.py", line 29, in __setup self.model.initialize(opt) File "/nfs_data/code/YTMT-Strategy-main/models/ytmt_model_demoire.py", line 242, in initialize self.load(self, opt.resume_epoch) File "/nfs_data/code/YTMT-Strategy-main/models/ytmt_model_demoire.py", line 413, in load model.net_i.load_state_dict(state_dict['icnn']) File "/opt/conda/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for YTMT_US: Missing key(s) in state_dict: "inc.ytmt_head.fusion_l.weight", "inc.ytmt_head.fusion_l.bias", "inc.ytmt_head.fusion_r.weight", "inc.ytmt_head.fusion_r.bias", "down1.model.ytmt_head.fusion_l.weight", "down1.model.ytmt_head.fusion_l.bias", "down1.model.ytmt_head.fusion_r.weight", "down1.model.ytmt_head.fusion_r.bias", "down2.model.ytmt_head.fusion_l.weight", "down2.model.ytmt_head.fusion_l.bias", "down2.model.ytmt_head.fusion_r.weight", "down2.model.ytmt_head.fusion_r.bias", "down3.model.ytmt_head.fusion_l.weight", "down3.model.ytmt_head.fusion_l.bias", "down3.model.ytmt_head.fusion_r.weight", "down3.model.ytmt_head.fusion_r.bias", "down4.model.ytmt_head.fusion_l.weight", "down4.model.ytmt_head.fusion_l.bias", "down4.model.ytmt_head.fusion_r.weight", "down4.model.ytmt_head.fusion_r.bias", "up1.model.ytmt_head.fusion_l.weight", "up1.model.ytmt_head.fusion_l.bias", "up1.model.ytmt_head.fusion_r.weight", "up1.model.ytmt_head.fusion_r.bias", "up2.model.ytmt_head.fusion_l.weight", "up2.model.ytmt_head.fusion_l.bias", "up2.model.ytmt_head.fusion_r.weight", "up2.model.ytmt_head.fusion_r.bias", "up3.model.ytmt_head.fusion_l.weight", "up3.model.ytmt_head.fusion_l.bias", "up3.model.ytmt_head.fusion_r.weight", "up3.model.ytmt_head.fusion_r.bias", "up4.model.ytmt_head.fusion_l.weight", "up4.model.ytmt_head.fusion_l.bias", "up4.model.ytmt_head.fusion_r.weight", "up4.model.ytmt_head.fusion_r.bias".

    opened by zdyshine 1
Owner
Qiming Hu
Qiming Hu
GitHub Repository
Hyperbolic Procrustes Analysis Using Riemannian Geometry

Hyperbolic Procrustes Analysis Using Riemannian Geometry The code in this repository creates the figures presented in this article: Please notice that

Ronen Talmon's Lab 2 Jan 08, 2023
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently This repository is the official implementat

VITA 4 Dec 20, 2022
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

Andrés Romero 37 Nov 27, 2022
这是一个利用facenet和retinaface实现人脸识别的库，可以进行在线的人脸识别。

Facenet+Retinaface：人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络，分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

chenxin 1 Dec 19, 2021
An SE(3)-invariant autoencoder for generating the periodic structure of materials

Crystal Diffusion Variational AutoEncoder This software implementes Crystal Diffusion Variational AutoEncoder (CDVAE), which generates the periodic st

Tian Xie 94 Dec 10, 2022
Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation

Shunted Transformer This is the offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation by Sucheng Ren, Daquan Zhou, Shengf

156 Dec 27, 2022
A Domain-Agnostic Benchmark for Self-Supervised Learning

DABS: A Domain Agnostic Benchmark for Self-Supervised Learning This repository contains the code for DABS, a benchmark for domain-agnostic self-superv

Alex Tamkin 81 Dec 09, 2022
PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Siyu Huang 33 Dec 06, 2022
Second-order Attention Network for Single Image Super-resolution (CVPR-2019)

Second-order Attention Network for Single Image Super-resolution (CVPR-2019) "Second-order Attention Network for Single Image Super-resolution" is pub

516 Dec 28, 2022
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

程星 87 Dec 24, 2022
InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing

InsTrim The paper: InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing Build Prerequisite llvm-8.0-dev clang-8.0 cmake = 3.2 Make git cl

75 Dec 23, 2022
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022
[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

Semantics Disentangling for Generalized Zero-shot Learning This is the official implementation for paper Zhi Chen, Yadan Luo, Ruihong Qiu, Zi Huang, J

25 Dec 06, 2022
Reproduced Code for Image Forgery Detection papers.

Image Forgery Detection With over 4.5 billion active internet users, the amount of multimedia content being shared every day has surpassed everyone’s

Umar Masud 15 Dec 06, 2022
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"

Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor

8 Sep 22, 2022
Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Implementation for Iso-Points (CVPR 2021) Official code for paper Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations paper |

Yifan Wang 66 Nov 08, 2022
[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

EOPSN: Exemplar-Based Open-Set Panoptic Segmentation Network (CVPR 2021) PyTorch implementation for EOPSN. We propose open-set panoptic segmentation t

Jaedong Hwang 49 Dec 30, 2022
PyTorch implementation of the ExORL: Exploratory Data for Offline Reinforcement Learning

ExORL: Exploratory Data for Offline Reinforcement Learning This is an original PyTorch implementation of the ExORL framework from Don't Change the Alg

Denis Yarats 52 Jan 01, 2023
RealFormer-Pytorch Implementation of RealFormer using pytorch

RealFormer-Pytorch Implementation of RealFormer using pytorch. Includes comparison with classical Transformer on image classification task (ViT) wrt C

Simo Ryu 90 Dec 08, 2022