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
Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation

NorCal Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation On Model Calibration for Long-Tailed Object Detec

Tai-Yu (Daniel) Pan 24 Dec 25, 2022
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

pyradiomics v3.0.1 Build Status Linux macOS Windows Radiomics feature extraction in Python This is an open-source python package for the extraction of

Artificial Intelligence in Medicine (AIM) Program 842 Dec 28, 2022
Learning to Estimate Hidden Motions with Global Motion Aggregation

Learning to Estimate Hidden Motions with Global Motion Aggregation (GMA) This repository contains the source code for our paper: Learning to Estimate

Shihao Jiang (Zac) 221 Dec 18, 2022
PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

Jinghuan Shang 14 Dec 09, 2022
Implementation of Axial attention - attending to multi-dimensional data efficiently

Axial Attention Implementation of Axial attention in Pytorch. A simple but powerful technique to attend to multi-dimensional data efficiently. It has

Phil Wang 250 Dec 25, 2022
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS

VITA 122 Dec 14, 2022
Accelerate Neural Net Training by Progressively Freezing Layers

FreezeOut A simple technique to accelerate neural net training by progressively freezing layers. This repository contains code for the extended abstra

Andy Brock 203 Jun 19, 2022
[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Discriminative Region-based Multi-Label Zero-Shot Learning (ICCV 2021) [arXiv][Project page coming soon] Sanath Narayan*, Akshita Gupta*, Salman Kh

Akshita Gupta 54 Nov 21, 2022
"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri

"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri Bu Github Reposundaki tüm projeler; kaleme almış olduğum "Projelerle Yapay Zekâ ve Bi

Ümit Aksoylu 4 Aug 03, 2022
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation CVPR2021(oral) [arxiv] Requirements python3.7 pytorch==

W-zx-Y 85 Dec 07, 2022
Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints". Edit 2021/

10 Dec 20, 2022
[CVPR 2022 Oral] Rethinking Minimal Sufficient Representation in Contrastive Learning

Rethinking Minimal Sufficient Representation in Contrastive Learning PyTorch implementation of Rethinking Minimal Sufficient Representation in Contras

36 Nov 23, 2022
Model Agnostic Interpretability for Multiple Instance Learning

MIL Model Agnostic Interpretability This repo contains the code for "Model Agnostic Interpretability for Multiple Instance Learning". Overview Executa

Joe Early 10 Dec 17, 2022
Benchmarks for Model-Based Optimization

Design-Bench Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box

Brandon Trabucco 43 Dec 20, 2022
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 04, 2022
This is the winning solution of the Endocv-2021 grand challange.

Endocv2021-winner [Paper] This is the winning solution of the Endocv-2021 grand challange. Dependencies pytorch # tested with 1.7 and 1.8 torchvision

Vajira Thambawita 14 Dec 03, 2022
DC540 hacking challenge 0x00005a.

dc540-0x00005a DC540 hacking challenge 0x00005a. PROMOTIONAL VIDEO - WATCH NOW HERE ON YOUTUBE CRITICAL PART 5A VIDEO - WATCH NOW HERE ON YOUTUBE Prio

Kevin Thomas 3 May 09, 2022
A python bot to move your mouse every few seconds to appear active on Skype, Teams or Zoom as you go AFK. 🐭 🤖

PyMouseBot If you're from GT and annoyed with SGVPN idle timeouts while working on development laptop, You might find this useful. A python cli bot to

Oaker Min 6 Oct 24, 2022
Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

zhanglabNKU 1 Nov 29, 2022
A high performance implementation of HDBSCAN clustering.

HDBSCAN HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates

2.3k Jan 02, 2023