How to Train a Custom Keypoint Detection Model with PyTorch (Article on Medium)

Mask R-CNN Model to detect the area of damage on a car. The rationale for such a model is that it can be used by insurance companies for faster processing of claims if users can upload pics and they can assess damage from them. This model can also be used by lenders if they are underwriting a car loan especially for a used car.

Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.

The tooth numbering module classifies and numbering dental objects detected as a result of segmentation according to the FDI notation used universally by dentists.

Coronary artery stenosis detection using Faster RCNN

This repo contains the process of getting started with Facebook FAIR's detectron2 project on windows 10 without any Nvidia GPU.

This study was published in 2022 in a scientific journal with SCI-Expanded index. The tooth numbering module uses the FDI notation, which is widely used by dentists, to classify and number dental items found as a result of segmentation. The performance of the Mask R–CNN method used has been proven by comparing it with other state-of-the-art meth…

Tensorflow-based framework which lists attentive implementation of the conventional neural network models (CNN, RNN-based), applicable for Relation Extraction classification tasks as well as API for custom model implementation

A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

In this assignment I have to build a Mask R-CNN based keypoint detector model using Detectron2. Detectron2 was written in PyTorch and contains many state-of-the-art obejct detection models with pretrained weights.

The Passport and ID Card Image Dataset is a collection of over 500 images of passports and ID cards, specifically created for the purpose of training RCNN models for image segmentation using Coco Annotator. The dataset includes high-quality images of passports and ID cards, covering a diverse range of countries, nationalities and designs.

PyTorch approach to object detection of wildifre smoke with Faster R-CNN inception v2 and SSD Mobilenet v2 Models and detailed comparative analysis between each other.

Exam Paper Analysis Project based on M-RCNN

These days we all are using mask just because of Covid 19. So i build this website to detect whether your image is using mask or not by using Computer Vision and Deep Learning Algorithm to detect the image.

Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.

Object detection using R-CNN model from scratch.

This work was part of detecting underground pipe detection using deep learning model called Faster R-CNN, was done as an Internship at CEREMA Laboratory, Angers, France. Supervised and guided by DAVID GUILBERT, Researcher and JAUFER RAKEEB, Phd student.

An object detection project implemented using Transfer Learning and R-CNN model. The object considered for this project is a "cellphone"

An IoT-based safety system utilising deep learning techniques to analyse environmental sounds in real-time. Employed sensors to capture audio signals and leveraged classification algorithms, including CNN-based and RNN-based models, for accurate sound recognition.

A real time soda can object detector using TensorFlow's Regions with Convolutional Neural Networks (R-CNN) adaptation integrated into Django web framework.