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59 search results found

Techniques ⭐ 7,795

Techniques for deep learning with satellite & aerial imagery

Ailab ⭐ 7,620

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

Notebooks ⭐ 3,677

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

Tensorflow2.0 Examples ⭐ 1,692

🙄 Difficult algorithm, Simple code.

Quickdraw ⭐ 851

Implementation of Quickdraw - an online game developed by Google

RMDL: Random Multimodel Deep Learning for Classification

Computer Vision Guide ⭐ 204

📖 This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

Machinelearning Ai ⭐ 184

This repository contains all the work that I regularly did and studied from Medium blogs, several research papers, and other Repos (related/unrelated to the research papers).

Iresnet ⭐ 157

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.

This is an official pytorch implementation of Fast Fourier Convolution.

Polynomial_nets ⭐ 131

Official Implementation of the CVPR'20 paper 'Π-nets: Deep Polynomial Neural Networks' and its T-PAMI-21 extension.

Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition"

LabelD is a quick and easy-to-use image annotation tool, built for academics, data scientists, and software engineers to enable single track or distributed image tagging. LabelD supports both localized, in-image (multi-)tagging, as well as image categorization.

Deep_learning_in_python_2018 ⭐ 114

Deep Learning workshop including image classification, face recognition, Object detection, language modelling, image captioning and neural machine translation.

Robot Grasp Detection ⭐ 97

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.

Torchflare ⭐ 83

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

Aideveloper ⭐ 74

GUI-based software for training, evaluating and applying deep neural nets for image classification

Pytorch Learners Tutorial ⭐ 71

PyTorch tutorial for learners

How To Train Your Neural Net ⭐ 55

Deep learning research implemented on notebooks using PyTorch.

Tensorflow For Beginners ⭐ 54

Introduction to deep learning with Tensorflow.

VFRAME: Visual Forensics and Metadata Extraction

Hotdog Or Not Hotdog ⭐ 49

Implementation of Hot Dog or not app from Silicon Valley (CNN to identify if the given picture is a hot dog or not)

Deep Learning ⭐ 48

Deep Learning Bootcamp

Deep Ranking ⭐ 47

Learning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same

An Image classifier to identify whether the given image is Batman or Superman using a CNN with high accuracy. (From getting images from google to saving our trained model for reuse.)

German Traffic Sign Classification Using Tensorflow ⭐ 40

In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

React Native Caffe2 ⭐ 39

React native package to use caffe2 models. Use deep learning on hybrid mobile application within 2 min

Pv_defect_detection ⭐ 38

Multi-label defect detection for Solar Cells from Electroluminescence images of the modules, using Deep Learning

Crowdlayer ⭐ 37

A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise.

Ocular Disease Intelligent Recognition Deep Learning ⭐ 35

ODIR-2019. Ocular Disease Intelligent Recognition Through Deep Learning Architectures

Cifar 10 ⭐ 32

Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.

Keras Application Zoo ⭐ 28

Reference implementations of popular DL models missing from keras-applications & keras-contrib

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

Resnet Rs Keras ⭐ 25

ResNet-RS models rewritten in Tensorflow / Keras functional API.

Alexnet A Practical Implementation ⭐ 24

Implementation of AlexNet with Tensorflow

Caltech_birds ⭐ 23

A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.

Reproducibilty Challenge Ecanet ⭐ 22

Unofficial Implementation of ECANets (CVPR 2020) for the Reproducibility Challenge 2020.

[WACV 2019 Oral] Deep Micro-Dictionary Learning and Coding Network

Image_classification_cifar 10 ⭐ 18

Image Classification on CIFAR-10 Dataset using Multi Layer Perceptrons in Python from Scratch.

Concise Ipython Notebooks For Deep Learning ⭐ 17

Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.

.NET Standard library for classifying pornographic images.

Personalised Aesthetic Assessment Using Residual Adapters ⭐ 16

Jupyter notebooks used as supporting material for an msc thesis about personalised aesthetic assessment using residual adapters.

Workshop Keras Dnn ⭐ 13

Deep Neural Network Architectures with dlib

Detection And Classification Of Alzheimers Disease ⭐ 12

The purpose of this paper is to detect Alzheimer’s Disease using Deep Learning and Machine Learning algorithms on the early basis which is being further optimized using CSA(Crow Search Algorithm). Alzheimer’s is one of kind and fatal. The early detection of Alzheimer’s Disease because of it’s progressive risk and patients all around the world. Early detection of AD is promising as it can help lot of patients to predetermine the condition they may face in future. AD being progressive, can be prev

Openvino_open_model_zoo_toolkit ⭐ 11

Python wrapper library for Intel distribution of OpenVINO and Open Model Zoo (OMZ) models. User can use DL by simple function calls with this library.

Image Classification ⭐ 10

Image classification with deep convolutional neural networks.

Image Classification With Pytorch ⭐ 9

Source Code of Project Image Classification with PyTorch 🔥

Sgd Uap Torch ⭐ 9

Universal Adversarial Perturbations (UAPs) for PyTorch

High Res Mapping ⭐ 9

High Resolution Class Activation Mapping for Discriminative Feature Localization

Boyorgirl ⭐ 8

A web application + neural net to distinguish baby boys and girls

Pytorch Cifar10 ⭐ 8

Image classification models on CIFAR10 dataset using pytorch

Official repository for the paper "Salient Mask-Guided Vision Transformer for Fine-Grained Classification" (VISIGRAPP '23)

Custom Intent ⭐ 7

create chatbots, image classification and linear regression models with three lines of code you can install it by running pip install CustomIntents

Nimbusml Presentation ⭐ 7

Real Time Traffic Sign Recognition ⭐ 7

Real time traffic sign classification using deep learning

Dnnviewer ⭐ 7

Deep Neural Network viewer

Face Mask Detection ⭐ 6

A real time face mask detection model

Pytorch Mixnet Ss ⭐ 5

Extremely light-weight MixNet with Top-1 75.7% and 2.5M params

Agent Crop ⭐ 5

Crop Disease Detection & Cure Suggestion System

Deepclassificationtool ⭐ 5

Deep image classification tool based on Keras. Tool implements light versions of VGG, ResNet and InceptionV3 for small images

Multi-component Image Translation for Deep Domain Generalization (Deep Domain Adaptation)

Fashion Mnist ⭐ 5

This repository contains the solution of Fashion MNIST solution using PyTorch

Visual Recognition Using Cnn ⭐ 5

Image classification using ConvNets models for Fashion-MNIST dataset.

Rest Api Neural Net ⭐ 5

REST-API based CNN deployed on heroku

Safeimage ⭐ 5

Tool to identify images with not Suitable For Work (NSFW) content

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