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
[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Y

118 Dec 26, 2022
Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

HW2 - ME 495 Overview Part 1: Makes the robot move in a figure 8 shape. The robot starts moving when launched on a real turtlebot3 and can be paused a

Devesh Bhura 0 Oct 21, 2022
Creating predictive checklists from data using integer programming.

Learning Optimal Predictive Checklists A Python package to learn simple predictive checklists from data subject to customizable constraints. For more

Healthy ML 5 Apr 19, 2022
Official Code for "Non-deep Networks"

Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Overview: Depth is the hallmark of DNNs. But more depth m

Ankit Goyal 567 Dec 12, 2022
KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

80 Dec 27, 2022
A library for answering questions using data you cannot see

A library for computing on data you do not own and cannot see PySyft is a Python library for secure and private Deep Learning. PySyft decouples privat

OpenMined 8.5k Jan 02, 2023
Train a deep learning net with OpenStreetMap features and satellite imagery.

DeepOSM Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data. DeepOSM can: Download a chunk of

TrailBehind, Inc. 1.3k Nov 24, 2022
Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Intelligent Robotics and Machine Vision Lab 4 Jul 19, 2022
Unofficial PyTorch Implementation of Multi-Singer

Multi-Singer Unofficial PyTorch Implementation of Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus. Requirements See re

SunMail-hub 123 Dec 28, 2022
Efficient face emotion recognition in photos and videos

This repository contains code of face emotion recognition that was developed in the RSF (Russian Science Foundation) project no. 20-71-10010 (Efficien

Andrey Savchenko 239 Jan 04, 2023
Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

Ross Girshick 7.8k Jan 03, 2023
SimplEx - Explaining Latent Representations with a Corpus of Examples

SimplEx - Explaining Latent Representations with a Corpus of Examples Code Author: Jonathan Crabbé ( Jonathan Crabbé 14 Dec 15, 2022

Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection

DDMP-3D Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection, a paper on CVPR2021. Instroduction T

Li Wang 32 Nov 09, 2022
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

ClevrTex This repository contains dataset generation code for ClevrTex benchmark from paper: ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi

Laurynas Karazija 26 Dec 21, 2022
Molecular Sets (MOSES): A benchmarking platform for molecular generation models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

Neelesh C A 3 Oct 14, 2022
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Nicholas Monath 35 Nov 16, 2022
Calling Julia from Python - an experiment on data loading

Calling Julia from Python - an experiment on data loading See the slides. TLDR After reading Patrick's blog post, we decided to try to replace C++ wit

Abel Siqueira 8 Jun 07, 2022
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
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend

Holy Wu 44 Dec 27, 2022