F

Faster R-CNN

Developed a virtual healthcare system for depression detection using multi-modal emotion analysis. Implemented CNN with Haar Cascade for facial emotion recognition and TF-IDF with Multinomial Naive Bayes for voice sentiment analysis, integrated with an emotion-aware chatbot to provide real-time emotional assessment and personalized

AI-powered face detection and recognition identifies faces in images or video using deep learning. It detects, extracts features, and matches them with stored data for secure and accurate identification.

A companion project to https://gitlab.com/sambryant/rosy-public for training feature tagged image datasets using RCNN from PyTorch.

Candidate Extraction in Two-stage Localization Models - implementation of RPN (Region Proposal Network), localization model proposed in Faster R-CNN. Implementation is in PyTorch deep learning framework. My undergraduate thesis computer vision project.

Evaluation of Fast, Faster and Mask R-CNN regarding their inference times

A simple NN that can increase the accuracy of existing object detectors.