Implementation of Focal Loss (Lin et al., 2017) with RetinaNet on the COCO dataset.

A simple and deeply commented PyTorch implementation of the RetinaNet paper, built as an educational resource. 📚 Demystify the core concepts of object detection with code that sticks closely to the original paper. 💡

Sample apps to demonstrate how to deploy models trained with TAO on DeepStream

Final Project Artificial Intelligence Class LA03

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

A Pythonic approach to object detection using Detectron2, a clean, modular framework for training and deploying computer vision models. DetectX simplifies the complexity of object detection while maintaining high performance and extensibility.

LUPI-OD - A novel methodology to improve object detection accuracy without increasing model size or complexity. | Master of Science Dissertation | University of Malta

SimpleAICV:pytorch training and testing examples.

[MICCAI 2024, top 11%] Official Pytorch implementation of Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

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.

RetinaNet-based AML and immune cell detection from multi-channel microscopy images.

The repository contains multiple algorithms for 1D and 2D barcode localization proposed in different papers in the past years. The repository contains the tools to measure the performance of those algorithms

Deep Learning-Based Medical Object Detection (IEEE Access)

This repository contains the deployment of a trained lung nodule detection model using YOLOv8 and RetinaNet architectures.

object-detection using tensorflow object detection api

Deep Learning for Automatic Pneumonia Detection, RSNA challenge

This is an object detection web app where users can upload an image, select a detection model, and view the processed image with detected objects. The result is downloadable for further use.

Automatic Pneunomia detection with deep neural network

Automatic Pneunomia detection with deep neural network

An AI-Powered Framework for Assessing Teacher Performance in Classroom Interactions: A Deep Learning Approach