OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
We write your reusable computer vision tools. 💜
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
Python library for YOLOv8 and YOLOv9 small object detection and instance segmentation
In browser YOLO Annotation Tool
Python Computer Vision & Video Analytics Framework With Batteries Included
A Computer Vision based system to detect fire at an early stage in real-time and alert the user through a mobile application.
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.
🛡️ A list of all profile badges and how to obtain each one 🛡️
ODLabel is a powerful tool for zero-shot object detection, labeling and visualization. It provides an intuitive graphical user interface for labeling objects in images using the YOLO-World model and supports various output formats such as YOLO, COCO, CSV, and XML.
C++ object detection inference from video or image input source
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
MkDocs plugin for Ultralytics Docs at https://docs.ultralytics.com
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Real-time object detection and counting with YOLOv3. Includes a user-friendly GUI for selecting image and video inputs.
This project aims to identify and monitor players, referees, and footballs in video footage using YOLO, a top-tier AI object detection model. The model should be trained further to enhance its efficiency. Moreover, categorization of players into teams based on their jersey colors, using Kmeans for pixel segmentation and clustering can be added.
Raspberry Pi stand-alone AI-powered camera with live feed, email notification and event-triggered cloud storage
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