One-stage and two-stage face detection models

Pneumonia Detection using Convolutional Neural Networks (RetinaNet)

This project is a part of my Multidisciplinary Project in semester 242 which aims to develop AI models for detecting diseases in bok choy plants. The project leverages deep learning techniques and distributed training to ensure scalability and efficiency.

It has never been rare among AI researchers that combine and evaluate different models to discover their methods. When I read about Retinanet and Efficientnet, I had a mind to combine them.

This repository contains code to train a Object detection model to detect Person/Car using RetinaNet model

An evaluation of three object detection algorithms on historical data

This project is completed as a fulfilment for the CDS590 Consultancy Project & Practicum provided by School of Computer Sciences, USM as part of their Masters of Science in Data Science and Analytics program.

An object detection approach for the identification of turtles' heads.

Face detection model

Computer Vision AI to perform object detection in aerial images taken from drones and small aircraft. Flask API to run inference in the cloud.

Training detection models (RetinaNet and SSD) to detect road objects, then applying a model to real world traffic video from Moscow.

Keras RetinaNet 工程实践

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

A Pytorch implementation of the Retinanet, based on the implementation by @yhenon

Official implementation of the papr 'A Study on Traffic Vulnerable Detection Using Object Detection-Based Esemble'

Итоговый проект. Реализация модели детекции и ее встраивание в реальное приложение на python

The Face Detection model is trained to detect faces in images and draw bounding boxes around them by utalizing retina-net model.

C# implementation of converting Pascal Voc label to RetinaNet label

Pre-trained coco model for detection with retinanet using camera to object detection.

Performance analysis of an object detector for blood cell detection