An Object Detection project based on RCNN-Algorithm using Python
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Updated
Jul 27, 2024 - Jupyter Notebook
An Object Detection project based on RCNN-Algorithm using Python
Transforming road safety with advanced statistical analysis and modeling. Join us in revolutionizing driver assistance systems!
A collection of computer vision projects, specifically covering image classification and object detection.
Repo containing computer vision object detection work to locate bacterial flagellar motors from 2D cryogenic electromagnetic images.
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.
In this repository is my experimental thesis work on the recognition of museum works through object detection techniques.
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.
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.
ORCA: Oceanic Recognition & Classification Application for sea-life analysis systems.
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
Coronary artery stenosis detection using Faster RCNN
Modifying pre-trained torchvision models.
How to Train a Custom Keypoint Detection Model with PyTorch (Article on Medium)
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.
Exam Paper Analysis Project based on M-RCNN
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.
A real time soda can object detector using TensorFlow's Regions with Convolutional Neural Networks (R-CNN) adaptation integrated into Django web framework.
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.
Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model
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