🩻 Detect lung nodules in CT scans using YOLOv8 and AWS SageMaker for early lung cancer diagnosis and efficient model deployment.

A type-safe, opinionated port of python's uniface: A Comprehensive Library for Face Detection, Recognition

A PyTorch Implementation of Feature Selective Anchor-Free Module for Single-Shot Object Detection (CVPR'19)

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

Modern PyTorch toolkit for the VisDrone aerial object detection dataset with production-ready training pipelines, real-time inference, and format converters. Features state-of-the-art models (Faster R-CNN, FCOS, RetinaNet), mixed precision training, rich progress tracking, and optimizations for small object detection in drone imagery.

An object detection pipeline using TensorFlow and KerasCV (RetinaNet) to identify manufacturing defects in jar lids. Features custom data processing and handling of ragged tensors for object detection.

Deep Learning for Automatic Pneumonia Detection, RSNA challenge

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

SimpleAICV:pytorch training and testing examples.

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

LUPI-OD - A novel methodology to improve object detection accuracy without increasing model size or complexity. | 2025 European Workshop on Visual Information Processing (EUVIP) | M.Sc. ICT (By Research) Dissertation | University of Malta

Object detection neural network architectures for underwater animal recognition.

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.

Implementations of state-of-the-art object detection models (Faster R-CNN, Mask R-CNN, SSD, RetinaNet, YOLOv3) on subsets of public datasets like COCO, Pascal VOC, OpenImages, and ImageNet.

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. 💡

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.

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.