A Deep-Learning model for image classification of a set of 10 food items. It uses the ResNet-50 pre-trained on ImageNet database.

Object Detection with FasterRCNN from Torchvision.

Successfully developed an object detection model using Faster R-CNN to detect safety helmets and ensure compliance at construction sites by accurately localizing helmets and personnel in real-time images.

Successfully developed an instance segmentation model using Mask R-CNN to detect and segment brain tumors from MRI scans with pixel-level precision.

Successfully developed a wildlife detection model using Faster R-CNN to identify and localize animals in natural habitats, supporting conservation efforts and ecological research.

Welcome to the AI-Powered CXR Diagnostic System! This project utilizes advanced AI and machine learning techniques to streamline the radiologist workflow by automating the analysis of chest X-ray (CXR) images.

Successfully developed an object detection model using Faster R-CNN to detect and localize wind turbines in aerial imagery, aiding in automated monitoring and infrastructure assessment.

Successfully developed an object detection model using Faster R-CNN to detect and classify traffic signs in road images, enhancing autonomous driving and intelligent transportation systems.

Developed an instance segmentation model using Mask R-CNN to accurately identify and segment germinated seeds in high-resolution seed images.

AI-powered search engine that uses FAISS and DenseNet-50 for both text and reverse image search capabilities. Comes with an asynchronous based web crawler

Successfully developed an object detection model using Faster R-CNN to detect vehicles and traffic-related objects in real-time road scenes, supporting smart traffic monitoring and surveillance applications.