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
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset

Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v

Meta Research 118 Jan 07, 2023
Code repo for EMNLP21 paper "Zero-Shot Information Extraction as a Unified Text-to-Triple Translation"

Zero-Shot Information Extraction as a Unified Text-to-Triple Translation Source code repo for paper Zero-Shot Information Extraction as a Unified Text

cgraywang 88 Dec 31, 2022
🎁 3,000,000+ Unsplash images made available for research and machine learning

The Unsplash Dataset The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of

Unsplash 2k Jan 03, 2023
DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Vehicle Indicator Toolset Deep Learning and Computer Vision based indicator toolset for vehicle drivers using live dash-cam footages. Tracking of vehi

Alex Xu 12 Dec 28, 2021
Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

1xN Pattern for Pruning Convolutional Neural Networks (paper) . This is Pytorch re-implementation of "1xN Pattern for Pruning Convolutional Neural Net

Mingbao Lin (林明宝) 29 Nov 29, 2022
Model Zoo for MindSpore

Welcome to the Model Zoo for MindSpore In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical

MindSpore 226 Jan 07, 2023
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Transformer-based models are widely used in natural language processi

Zhanpeng Zeng 12 Jan 01, 2023
Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Introduction This is a PyTorch implementation of the following research papers: (1) Hierarchical Text Generation and Planning for Strategic Dialogue (

Facebook Research 1.4k Dec 29, 2022
Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Emile van Krieken 140 Dec 30, 2022
Potato Disease Classification - Training, Rest APIs, and Frontend to test.

Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt

codebasics 95 Dec 21, 2022
An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

Andrew Jesson 9 Apr 04, 2022
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation This is the official repository for our paper Neural Reprojection Error

Hugo Germain 78 Dec 01, 2022
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Monk - A computer vision toolkit for everyone Why use Monk Issue: Want to begin learning computer vision Solution: Start with Monk's hands-on study ro

Tessellate Imaging 507 Dec 04, 2022
This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning It includes /bert, which is the original BERT repos

Mitchell Gordon 11 Nov 15, 2022
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
SIMULEVAL A General Evaluation Toolkit for Simultaneous Translation

SimulEval SimulEval is a general evaluation framework for simultaneous translation on text and speech. Requirement python = 3.7.0 Installation git cl

Facebook Research 48 Dec 28, 2022
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan 70 Dec 18, 2022
A facial recognition doorbell system using a Raspberry Pi

Facial Recognition Doorbell This project expands on the person-detecting doorbell system to allow it to identify faces, and announce names accordingly

rydercalmdown 22 Apr 15, 2022
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, L

Billy HE 141 Dec 30, 2022
Nightmare-Writeup - Writeup for the Nightmare CTF Challenge from 2022 DiceCTF

Nightmare: One Byte to ROP // Alternate Solution TLDR: One byte write, no leak.

1 Feb 17, 2022