tuvovan / Nhdrrnet
Licence: mit
Keras Implementation of the paper Deep HDR Imaging via A Non-Local Network - TIP 2020
Stars: ✭ 37
Programming Languages
python
139335 projects - #7 most used programming language
Labels
Projects that are alternatives of or similar to Nhdrrnet
Segmentation models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Stars: ✭ 3,575 (+9562.16%)
Mutual labels: keras-tensorflow
Labml
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Stars: ✭ 508 (+1272.97%)
Mutual labels: keras-tensorflow
Otto
Otto makes machine learning an intuitive, natural language experience. 🏆 Facebook AI Hackathon winner ⭐️ #1 Trending on MadeWithML.com ⭐️ #4 Trending JavaScript Project on GitHub ⭐️ #15 Trending (All Languages) on GitHub
Stars: ✭ 894 (+2316.22%)
Mutual labels: keras-tensorflow
Chineseaddress ocr
Photographing Chinese-Address OCR implemented using CTPN+CTC+Address Correction. 拍照文档中文地址文字识别。
Stars: ✭ 309 (+735.14%)
Mutual labels: keras-tensorflow
Attention Is All You Need Keras
A Keras+TensorFlow Implementation of the Transformer: Attention Is All You Need
Stars: ✭ 628 (+1597.3%)
Mutual labels: keras-tensorflow
Resnetcam Keras
Keras implementation of a ResNet-CAM model
Stars: ✭ 269 (+627.03%)
Mutual labels: keras-tensorflow
Dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
Stars: ✭ 912 (+2364.86%)
Mutual labels: keras-tensorflow
Introneuralnetworks
Introducing neural networks to predict stock prices
Stars: ✭ 486 (+1213.51%)
Mutual labels: keras-tensorflow
Face Mask Detection
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
Stars: ✭ 774 (+1991.89%)
Mutual labels: keras-tensorflow
Deepcut
A Thai word tokenization library using Deep Neural Network
Stars: ✭ 330 (+791.89%)
Mutual labels: keras-tensorflow
Invoice
增值税发票OCR识别,使用flask微服务架构,识别type:增值税电子普通发票,增值税普通发票,增值税专用发票;识别字段为:发票代码、发票号码、开票日期、校验码、税后金额等
Stars: ✭ 381 (+929.73%)
Mutual labels: keras-tensorflow
Kafka Streams Machine Learning Examples
This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.
Stars: ✭ 661 (+1686.49%)
Mutual labels: keras-tensorflow
Amazing Semantic Segmentation
Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)
Stars: ✭ 309 (+735.14%)
Mutual labels: keras-tensorflow
Deep Music Genre Classification
🎵 Using Deep Learning to Categorize Music as Time Progresses Through Spectrogram Analysis
Stars: ✭ 23 (-37.84%)
Mutual labels: keras-tensorflow
Dinoruntutorial
Accompanying code for Paperspace tutorial "Build an AI to play Dino Run"
Stars: ✭ 285 (+670.27%)
Mutual labels: keras-tensorflow
Music recommender
Music recommender using deep learning with Keras and TensorFlow
Stars: ✭ 528 (+1327.03%)
Mutual labels: keras-tensorflow
Densedepth
High Quality Monocular Depth Estimation via Transfer Learning
Stars: ✭ 963 (+2502.7%)
Mutual labels: keras-tensorflow
Tf Keras Surgeon
Pruning and other network surgery for trained TF.Keras models.
Stars: ✭ 25 (-32.43%)
Mutual labels: keras-tensorflow
Pytorch2keras
PyTorch to Keras model convertor
Stars: ✭ 676 (+1727.03%)
Mutual labels: keras-tensorflow
Deep HDR Imaging
The Keras Implementation of the Deep HDR Imaging via A Non-Local Network - TIP 2020
Content
Getting Started
- Clone the repository
Prerequisites
- Tensorflow 2.2.0+
- Tensorflow_addons
- Python 3.6+
- Keras 2.3.0
- PIL
- numpy
pip install -r requirements.txt
Running
Training
-
Preprocess
-
Download the training data and testing data.
-
Run this file to generate data. (Please remember to change path first)
python src/create_dataset.py
-
-
Train NHDRRNet
python main.py
-
Test NHDRRNet
python test.py
Usage
Training
usage: main.py [-h] [--images_path IMAGES_PATH] [--test_path TEST_PATH]
[--lr LR] [--gpu GPU] [--num_epochs NUM_EPOCHS]
[--train_batch_size TRAIN_BATCH_SIZE]
[--display_ep DISPLAY_EP] [--checkpoint_ep CHECKPOINT_EP]
[--checkpoints_folder CHECKPOINTS_FOLDER]
[--load_pretrain LOAD_PRETRAIN] [--pretrain_dir PRETRAIN_DIR]
[--filter FILTER] [--kernel KERNEL]
[--encoder_kernel ENCODER_KERNEL]
[--decoder_kernel DECODER_KERNEL]
[--triple_pass_filter TRIPLE_PASS_FILTER]
optional arguments: -h, --help show this help message and exit
--images_path training path
--lr LR
--gpu GPU
--num_epochs NUM of EPOCHS
--train_batch_size training batch size
--display_ep display result every "x" epoch
--checkpoint_ep save weights every "x" epoch
--checkpoints_folder folder to save weight
--load_pretrain load pretrained model
--pretrain_dir pretrained model folder
--filter default filter
--kernel default kernel
--encoder_kernel encoder filter size
--decoder_kernel decoder filter size
--triple_pass_filter number of filter in triple pass
Testing
The weight file was deprecated. Will be updated soon.
usage: test.py [-h] [--test_path TEST_PATH] [--gpu GPU]
[--weight_test_path WEIGHT_TEST_PATH] [--filter FILTER]
[--kernel KERNEL] [--encoder_kernel ENCODER_KERNEL]
[--decoder_kernel DECODER_KERNEL]
[--triple_pass_filter TRIPLE_PASS_FILTER]
optional arguments: -h, --help show this help message and exit
--test_path test path
--weight_test_path weight test path
--filter default filter
--kernel default kernel
--encoder_kernel encoder filter size
--decoder_kernel decoder filter size
--triple_pass_filter number of filter in triple pass
Result
License
This project is licensed under the MIT License - see the LICENSE file for details
References
[1] Deep HDR Imaging via A Non-Local Network - TIP 2020 link
[3] Training and Testing dataset - link
Citation
@ARTICLE{8989959, author={Q. Yan and L. Zhang and Y. Liu and Y. Zhu and J. Sun and Q. Shi and Y. Zhang},
journal={IEEE Transactions on Image Processing},
title={Deep HDR Imaging via A Non-Local Network},
year={2020},
volume={29},
number={},
pages={4308-4322},}
Acknowledgments
- This work based on the paper mentioned above with few modification:
- the fixed size of the adaptive average pooling (16 instead of 32 as assigned in the paper)
- the number of triple pass module is defined as 10 to match the number of 32M as stated in the paper.
- Any ideas on updating or misunderstanding, please send me an email: [email protected]
- If you find this repo helpful, kindly give me a star.
https://github.com/tuvovan/ANL-HDRI
Update: I have just released my work on HDR imaging using Attention non-local network. Please check as follow:Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].