Implementation of Libra R-CNN: Towards Balanced Learning for Object Detection
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Updated
Nov 11, 2020
Implementation of Libra R-CNN: Towards Balanced Learning for Object Detection
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.
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.
An object detection project implemented using Transfer Learning and R-CNN model. The object considered for this project is a "cellphone"
This repo contains the process of getting started with Facebook FAIR's detectron2 project on windows 10 without any Nvidia GPU.
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.
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.
Useful code for the users of the food flipping dataset, published in Nature Scientific Data.
The tooth numbering module classifies and numbering dental objects detected as a result of segmentation according to the FDI notation used universally by dentists.
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…
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.
RCNN based Model Training
Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model
Object detection using R-CNN model from scratch.
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.
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.
Exam Paper Analysis Project based on M-RCNN
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.
How to Train a Custom Keypoint Detection Model with PyTorch (Article on Medium)
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