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.

Coronary artery stenosis detection using Faster RCNN

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

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.

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

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.

Repo containing computer vision object detection work to locate bacterial flagellar motors from 2D cryogenic electromagnetic images.

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.

Modifying pre-trained torchvision models.

Detecting wildfire-affected areas using satellite imagery with computer vision techniques , highlighting the efficiency of Faster RCNN model.

Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model

A fusion model combining object detection and tracking algorithms to enhance object tracking accuracy. Includes performance metrics and visual results. Download required model weights and test videos from the provided link.

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

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

This project is centered around leveraging CRNN (Convolution Recurrent Neural Networks) and Digital Image Processing principles to extract license plates from car images and convert them to text. By using advanced AI algorithms and computer vision techniques, the project aims to provide a reliable and accurate way to recognize license plates.

Implementation of Libra R-CNN: Towards Balanced Learning for Object Detection