Okanagan Marine Design's first entry to the RoboSub competition, powered by ROS2, Sparese Voxel Grids, YoloV8, and PyTrees

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference

The climbing crux model is a machine-learning project that aims to recognize climbing holds and the distance between them from a photo and suggest routes that fit the user's climbing level.

NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

Profile PyTorch models for FLOPs and parameters, helping to evaluate computational efficiency and memory usage.

This Repo helps user Unlock the "Pair Extraordinaire" Achievement.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models.

Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural Network and YOLO by changing its backbone

An automatic surveillance system for PPE compliance detection using Streamlit

Ultralytics assets

C++ object detection inference from video or image input source

Object Detection in OBS, real-time, local, GPU optional

Convenient for individuals to annotate images and check YOLO format labels

A simpler (and working) notebook and repo for convinient use of YOLO-NAS-POSE, a human pose estimation model by Deci AI. Aims to run in a less resource intensive manner, by eliminating real-time capture and playback, and instead reading from and writing to permanent image and video files.

This repository provides a script to convert an ONNX model to a PyTorch model. The script includes functions to load an ONNX model, convert it to a PyTorch model, and save the converted model.

The "you only glance once" object detection model

SafeAR - Supporting privacy and confidentiality in augmented reality context Repo

This project uses YOLO models for efficient object detection with a Streamlit interface. Users can upload images or video streams for real-time detection. It supports YOLOv7, YOLOv8, and YOLOv9, offering flexibility and high accuracy in various scenarios.