432 Repositories
Python convolutional-kernel Libraries
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) This is the official implementation of Focals Conv (CVPR 2022), a new sp
Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement
MTFAA-Net Unofficial PyTorch implementation of Baidu's MTFAA-Net: "Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speec
Code for intrusion detection system (IDS) development using CNN models and transfer learning
Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus
Visualizing Yolov5's layers using GradCam
YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di
A Traffic Sign Recognition Project which can help the driver recognise the signs via text as well as audio. Can be used at Night also.
Traffic-Sign-Recognition In this report, we propose a Convolutional Neural Network(CNN) for traffic sign classification that achieves outstanding perf
Includes PyTorch - Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.
ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo
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
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"
Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress
CVPR 2022 "Online Convolutional Re-parameterization"
OREPA: Online Convolutional Re-parameterization This repo is the PyTorch implementation of our paper to appear in CVPR2022 on "Online Convolutional Re
Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃
This repository provides a library for efficient training of masked language models (MLM), built with fairseq. We fork fairseq to give researchers mor
Official implementation of Unfolded Deep Kernel Estimation for Blind Image Super-resolution.
Unfolded Deep Kernel Estimation for Blind Image Super-resolution Hongyi Zheng, Hongwei Yong, Lei Zhang, "Unfolded Deep Kernel Estimation for Blind Ima
Experimental Python implementation of OpenVINO Inference Engine (very slow, limited functionality). All codes are written in Python. Easy to read and modify.
PyOpenVINO - An Experimental Python Implementation of OpenVINO Inference Engine (minimum-set) Description The PyOpenVINO is a spin-off product from my
Implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networks, using PyTorch
C-CNN: Contourlet Convolutional Neural Networks This repo implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networ
Map Ookla server locations as a Kernel Density Estimation (KDE) geographic map plot.
Ookla Server KDE Plotting This notebook was created to map Ookla server locations as a Kernel Density Estimation (KDE) geographic map plot. Currently,
Convolutional Neural Network to detect deforestation in the Amazon Rainforest
Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen
Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN)
Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN) This code implements the skeleton-based action segmentation MS-GCN model from Autom
A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.
CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition Paper: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition accepted fo
DiffStride: Learning strides in convolutional neural networks
DiffStride is a pooling layer with learnable strides. Unlike strided convolutions, average pooling or max-pooling that require cross-validating stride values at each layer, DiffStride can be initialized with an arbitrary value at each layer (e.g. (2, 2) and during training its strides will be optimized for the task at hand.
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
TDY-CNN for Text-Independent Speaker Verification Official implementation of Temporal Dynamic Convolutional Neural Network for Text-Independent Speake
Semantic Segmentation Architectures Implemented in PyTorch
pytorch-semseg Semantic Segmentation Algorithms Implemented in PyTorch This repository aims at mirroring popular semantic segmentation architectures i
Example of semantic segmentation in Keras
keras-semantic-segmentation-example Example of semantic segmentation in Keras Single class example: Generated data: random ellipse with random color o
This is a model made out of Neural Network specifically a Convolutional Neural Network model
This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.
Convolutional neural network that analyzes self-generated images in a variety of languages to find etymological similarities
This project is a convolutional neural network (CNN) that analyzes self-generated images in a variety of languages to find etymological similarities. Specifically, the goal is to prove that computer vision can be used to identify cognates known to exist, and perhaps lead linguists to evidence of unknown cognates.
This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs)
Description This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs) in [Gardy et
traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.
traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to
A Python Jupyter Kernel in Slack. Just send Python code as a message.
Slack IPython bot 🤯 One Slack bot to rule them all. PyBot. Just send Python code as a message. Install pip install slack-ipython To start the bot, si
Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers
Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 The repository provides 100+ Pap
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt
On the adaptation of recurrent neural networks for system identification
On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape
Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning🕵🏻♂️
This is a Kaggle competition in which we have to identify if the given lesion image is malignant or not for Melanoma which is a type of skin cancer.
Snek-test - An operating system kernel made in python and assembly
pythonOS An operating system kernel made in python and assembly Wait what? It us
Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation
SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by
List of awesome things around semantic segmentation 🎉
Awesome Semantic Segmentation List of awesome things around semantic segmentation 🎉 Semantic segmentation is a computer vision task in which we label
Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images
Lung Segmentation (2D) Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images. Demo See the application of the
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments
Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
ild-cnn This is supplementary material for the manuscript: "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neur
Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA)
Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA). Master's thesis documents. Bibliography, experiments and reports.
Semantic Segmentation for Aerial Imagery using Convolutional Neural Network
This repo has been deprecated because whole things are re-implemented by using Chainer and I did refactoring for many codes. So please check this newe
Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks
This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi
Source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree.
self-driving-car In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree. Hope this might
CN24 is a complete semantic segmentation framework using fully convolutional networks
Build status: master (production branch): develop (development branch): Welcome to the CN24 GitHub repository! CN24 is a complete semantic segmentatio
Segment axon and myelin from microscopy data using deep learning
Segment axon and myelin from microscopy data using deep learning. Written in Python. Using the TensorFlow framework. Based on a convolutional neural network architecture. Pixels are classified as either axon, myelin or background.
DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency
[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"
Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat
To prepare an image processing model to classify the type of disaster based on the image dataset
Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas
This module is used to create Convolutional AutoEncoders for Variational Data Assimilation
VarDACAE This module is used to create Convolutional AutoEncoders for Variational Data Assimilation. A user can define, create and train an AE for Dat
Code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks
Biomedical Entity Linking This repo provides the code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Res
TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain
TCNN Pandey A, Wang D L. TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain[C]//ICASSP 2019-2019 IEEE Int
Proof of concept of CVE-2022-21907 Double Free in http.sys driver, triggering a kernel crash on IIS servers
CVE-2022-21907 - Double Free in http.sys driver Summary An unauthenticated attacker can send an HTTP request with an "Accept-Encoding" HTTP request he
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"
Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment
OneShot Learning-based hotword detection.
EfficientWord-Net Hotword detection based on one-shot learning Home assistants require special phrases called hotwords to get activated (eg:"ok google
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
BAM and CBAM Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)" Updat
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)
Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio
Unofficial JAX implementations of Deep Learning models
JAX Models Table of Contents About The Project Getting Started Prerequisites Installation Usage Contributing License Contact About The Project The JAX
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt
TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network Created by Seunghoon Hong, Junhyuk Oh,
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Weakly Supervised Segmentation with TensorFlow This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described
Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler
Part Detector Discovery This is the code used in our paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodne
Food recognition model using convolutional neural network & computer vision
Food recognition model using convolutional neural network & computer vision. The goal is to match or beat the DeepFood Research Paper
A Haskell kernel for IPython.
IHaskell You can now try IHaskell directly in your browser at CoCalc or mybinder.org. Alternatively, watch a talk and demo showing off IHaskell featur
Practical Machine Learning with Python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
🎃 Core identification module of AI powerful point reading system platform.
ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks arXiv preprint: https://arxiv.org/abs/2201.02143. Architec
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
🙄 Difficult algorithm, Simple code.
🎉TensorFlow2.0-Examples🎉! "Talk is cheap, show me the code." ----- Linus Torvalds Created by YunYang1994 This tutorial was designed for easily divin
A High-Quality Real Time Upscaler for Anime Video
Anime4K Anime4K is a set of open-source, high-quality real-time anime upscaling/denoising algorithms that can be implemented in any programming langua
This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation).
FlatGCN This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation, submitted to ICASSP2022). Req
Fully Convlutional Neural Networks for state-of-the-art time series classification
Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin
Deep Learning for Time Series Classification
Deep Learning for Time Series Classification This is the companion repository for our paper titled "Deep learning for time series classification: a re
U-Time: A Fully Convolutional Network for Time Series Segmentation
U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig
Implementation of Convolutional LSTM in PyTorch.
ConvLSTM_pytorch This file contains the implementation of Convolutional LSTM in PyTorch made by me and DavideA. We started from this implementation an
TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset
AlexNet training on ImageNet LSVRC 2012 This repository contains an implementation of AlexNet convolutional neural network and its training and testin
Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks for Sentence Classification Code for the paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). R
U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch
U-Net Implementation By Christopher Ley This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical
An image classification app boilerplate to serve your deep learning models asap!
Image 🖼 Classification App Boilerplate Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and ho
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.
CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr
Fashion Entity Classification
Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
DCGAN-tensorflow - A tensorflow implementation of Deep Convolutional Generative Adversarial Networks
DCGAN in Tensorflow Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networ
Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model
Deep Inside Convolutional Networks This is a caffe implementation to visualize the learnt model. Part of a class project at Georgia Tech Problem State
OverFeat is a Convolutional Network-based image classifier and feature extractor.
OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat
Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition
Convolutional Two-Stream Network Fusion for Video Action Recognition
Deep-Learning-Image-Captioning - Implementing convolutional and recurrent neural networks in Keras to generate sentence descriptions of images
Deep Learning - Image Captioning with Convolutional and Recurrent Neural Nets ========================================================================
Computer-Vision-Paper-Reviews - Computer Vision Paper Reviews with Key Summary along Papers & Codes
Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 50+ Papers across Computer Visio
A video scene detection algorithm is designed to detect a variety of different scenes within a video
Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logically and chronologically related shots taken in a specific order to depict an over-arching concept or story.
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai
Coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow
TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar
Saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Saliency Methods 🔴 Now framework-agnostic! (Example core notebook) 🔴 🔗 For further explanation of the methods and more examples of the resulting ma
Tensorflow-Project-Template - A best practice for tensorflow project template architecture.
Tensorflow Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributi
Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend
Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend This project acts as both a tuto
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne
Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Semi-supervised Deep Kernel Learning This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data
A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling"
SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th
An implementation of Deep Graph Infomax (DGI) in PyTorch
DGI Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341 Overview Here we provide an implementation of Deep Graph Infom
A Bot to Track Kernel Upstreams from kernel.org and Post it on Telegram Channel
Channel Kernel Tracker is the channel where the bot will be sending the updates in. Introduction This is a Telegram Bot to Track Kernel Upstreams kern
The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".
Block Modeling-Guided Graph Convolutional Neural Networks This repository contains the demo code of the paper: Block Modeling-Guided Graph Convolution
Deeper insights into graph convolutional networks for semi-supervised learning
deeper_insights_into_GCNs Deeper insights into graph convolutional networks for semi-supervised learning References data and utils.py come from Implem