This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.

Notebooks for detection and classification model training. Insect classification model. Python scripts for processing of data, collected with the Insect Detect DIY camera trap.

a simple notebook that allows for easy experimentation with the YOLOv5 object detection algorithm and YouTube Bounding Box dataset

Jupyter notebook for YOLOv5 custom dataset training for Facemask detection in Pytorch.

This repository contains notebooks for training and testing yolov5/X and also contains visualization code.

Notebooks to fine-tune Yolov5 and Yolov8 and export model for JavaScript deployment

This notebook shows training on the Blood Cell Dataset (BCCD). This technologoy will become easily accessible to any developer wishing to use computer vision in their projects.

Repository documenting YOLOv5 training on Gazebo-simulated marine markers, with detailed Jupyter notebooks and stored model weights for enhanced object detection.

Repository showcasing YOLOv5 training on a custom dataset of real-world marine markers, featuring comprehensive Jupyter notebooks and archived model weights for advanced object detection in marine environments.

This project implements a YOLOv5-based image object detection model using a Jupyter Notebook. It provides a step-by-step guide to setting up the environment, preparing data, building and training the model, and analyzing results. The project aims to demonstrate the effectiveness of YOLOv5 in detecting objects in images efficiently.