You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Semi-automatic YOLO dataset preparation. Use a pre-trained object detection model to annotate your dataset, then review each image and 'accept' or 'refuse'. Can also edit/mark bounding boxes by hand. Store project data in a single .csv file.
A real-time object detection system that identifies and classifies objects in images or video streams using deep learning models like YOLO, Faster R-CNN, or SSD. Built with TensorFlow and OpenCV, it supports applications in surveillance, autonomous vehicles, and more.
An object detection model based on YOLOv8 to detect and localize mangoes in images and videos. This project covers image annotation, transfer learning with pretrained weights, and evaluation using a public dataset from Hugging Face, manually annotated for mango detection.
Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).