Unofficial PyTorch Implementation of AHDRNet (CVPR 2019)

Overview

AHDRNet-PyTorch

Framework

1. Environment

  • Python >= 3.7
  • PyTorch >= 1.4.0
  • opencv-python = 4.5.1
  • imageio = 2.13.3
  • matplotlib

2. Dataset

The training data and testing data is from Kalantari (ACM TOG 2017), the dataset can be downloade from Kalantari Dataset.

3. Quick Demo (Only for tiff format 48-depth images (same with the Kalantari Dataset) now, others in progress)

  1. Clone this repository:
    git clone https://github.com/ytZhang99/AHDRNet-PyTorch.git
    
  2. Place the test image folders in ./data/Test/:
    Test
    └── test_data
        ├── Name_A
        |   ├── 1.tif
        |   ├── 2.tif
        |   ├── 3.tif
        |   ├── exposure.txt
        |   └── HDRImg.hdr (optional)
        └── Name_B
    
  3. Run the following command to test:
    python main.py --test_only
    
    The output images are placed in ./results/0_epoch/

4. Training

  1. Place the training image folders in ./data/Train/:
    Train
    └── train_data
        ├── Name_A
        |   ├── 1.tif
        |   ├── 2.tif
        |   ├── 3.tif
        |   ├── exposure.txt
        |   └── HDRImg.hdr
        └── Name_B
    
  2. Modify the main.sh file and run the following command to train:
    sh main.sh
    
    Notice that the default setting of this program is implementing validation on the test dataset after training, you can modify main.sh to close the validation progress.
  3. The trained model is saved in ./ckp/, then you can test your own model:
    python main.py --test_only --model latest.pth
    python main.py --test_only --model best_checkpoint.pth (This model is accessible with validation)
    
Owner
Yutong Zhang
Yutong Zhang
Code and data for "TURL: Table Understanding through Representation Learning"

TURL This Repo contains code and data for "TURL: Table Understanding through Representation Learning". Environment and Setup Data Pretraining Finetuni

SunLab-OSU 63 Nov 23, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022
Continuous Security Group Rule Change Detection & Response at scale

Introduction Get notified of Security Group Changes across all AWS Accounts & Regions in an AWS Organization, with the ability to respond/revert those

Raajhesh Kannaa Chidambaram 3 Aug 13, 2022
This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning"

CSP_Deep_EEG This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning" {https://www

Seyed Mahdi Roostaiyan 2 Nov 08, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
This is a project based on retinaface face detection, including ghostnet and mobilenetv3

English | 简体中文 RetinaFace in PyTorch Chinese detailed blog:https://zhuanlan.zhihu.com/p/379730820 Face recognition with masks is still robust---------

pogg 59 Dec 21, 2022
An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

Xingdong Zuo 12 Dec 02, 2022
Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

Zhezheng Luo 9 Jun 30, 2022
Fast and Simple Neural Vocoder, the Multiband RNNMS

Multiband RNN_MS Fast and Simple vocoder, Multiband RNN_MS. Demo Quick training How to Use System Details Results References Demo ToDO: Link super gre

tarepan 5 Jan 11, 2022
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Contributors of this repo: Zhibo Zhang ( Zhibo (Darren) Zhang 18 Nov 01, 2022

simple demo codes for Learning to Teach with Dynamic Loss Functions

Learning to Teach with Dynamic Loss Functions This repo contains the simple demo for the NeurIPS-18 paper: Learning to Teach with Dynamic Loss Functio

Lijun Wu 15 Dec 30, 2021
PyElecCL - Electron Monte Carlo Second Checks

PyElecCL Python program to perform second checks for electron Monte Carlo radiat

Reese Haywood 3 Feb 22, 2022
Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection Introduction This project inherits the property of our pytorc

Jianwei Yang 912 Dec 21, 2022
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

DeepMLS: Deep Implicit Moving Least-Squares Functions for 3D Reconstruction This repository contains the implementation of the paper: Deep Implicit Mo

103 Dec 22, 2022
Real life contra a deep learning project built using mediapipe and openc

real-life-contra Description A python script that translates the body movement into in game control. Welcome to all new real life contra a deep learni

Programminghut 7 Jan 26, 2022
Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.

VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks. VMAgent is constructed based on one month r

56 Dec 12, 2022
Source code for Fixed-Point GAN for Cloud Detection

FCD: Fixed-Point GAN for Cloud Detection PyTorch source code of Nyborg & Assent (2020). Abstract The detection of clouds in satellite images is an ess

Joachim Nyborg 8 Dec 22, 2022
使用OpenCV部署全景驾驶感知网络YOLOP,可同时处理交通目标检测、可驾驶区域分割、车道线检测,三项视觉感知任务,包含C++和Python两种版本的程序实现。本套程序只依赖opencv库就可以运行, 从而彻底摆脱对任何深度学习框架的依赖。

YOLOP-opencv-dnn 使用OpenCV部署全景驾驶感知网络YOLOP,可同时处理交通目标检测、可驾驶区域分割、车道线检测,三项视觉感知任务,依然是包含C++和Python两种版本的程序实现 onnx文件从百度云盘下载,链接:https://pan.baidu.com/s/1A_9cldU

178 Jan 07, 2023
Dynamic wallpaper generator.

Wiki • About • Installation About This project is a dynamic wallpaper changer. It waits untill you turn on the music, downloads album cover if it's po

3 Sep 18, 2021
CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image.

CoReNet CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image. It produces coherent reconstructions, where all objec

Google Research 80 Dec 25, 2022