YOLOv8

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YOLO v8

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

What's new in YOLOv8?

YOLOv8 supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification. This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

General
Relese date	May, 2023
Repository	https://github.com/ultralytics/ultralytics
Type	Real time object detection

Libraries

Discover YOLOv8

Documentation YOLOv8:
Quickstart Quickstart with YOLOv8
GitHub Repository View the GitHub repository for YOLOv8
Hugging Face Spaces Test YOLOv8 in the browser with Hugging Face Spaces

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YOLO YOLOv8 AI technology Hackathon projects

Discover innovative solutions crafted with YOLO YOLOv8 AI technology, developed by our community members during our engaging hackathons.

VisionLink AI: Intelligent Security OS

VisionLink AI: Intelligent Security Operating System is an advanced AI-powered security architecture designed to transform passive surveillance into active threat detection. Traditional CCTV frameworks fail to prevent violence because they only record incidents as they unfold, leaving security teams reactive rather than proactive. VisionLink AI bridges this gap by deploying an automated real-time tracking engine directly integrated with live video streams. Utilizing state-of-the-art computer vision logic and customized object detection parameters through modern frameworks like YOLOv8, the system constantly monitors camera feeds to isolate and instantly flag dynamic weapons, tactical firearms, and suspicious physical behavior. Once a weapon or potential threat is detected, the intelligent agent bypasses manual oversight to generate immediate, cloud-based emergency notifications for security operation centers, ensuring quick mitigation, preventing casualties, and safeguarding high-risk corporate and public environments globally.

Zynaptrix Context-Aware RAG (Gemini AI)

Zynaptrix is a context-aware RAG system built around Google Gemini and a modular agent pipeline to convert heterogeneous documents into accurate, professional reports. The backend ingests PDFs and images, uses a captioner, figure-splitter, and table transformer to extract structured content, then creates embeddings and indexes for fast retrieval. An orchestrator agent composes retrieved context with Gemini-powered synthesis; a report-writer agent formats outputs into exportable documents while a safety-critic agent enforces guardrails. The architecture separates ingestion, embedding, retrieval, and generation concerns so teams can add new extractors or retrievers easily. The frontend provides an assistant API and machine-focused workflows for interactive querying and batch report generation. Designed for research and enterprise use, Zynaptrix emphasizes provenance (source links and extracted snippets), multimodal understanding (text, figures, tables), and reproducible report generation for knowledge workers, analysts, and technical communicators

SafeSite AI

THE PROBLEM Every 3 minutes a construction worker is injured in the U.S. and every 8 hours one dies. Human supervisors cannot watch every corner of a 5‑acre site; it’s a biological limit, not negligence. Meanwhile traditional AI solutions hallucinate bounding boxes and cause alarm fatigue. OUR SOLUTION SafeSite AI is a dual‑layer safety pipeline purpose‑built for construction sites. The “eyes” of the system are YOLOv8, a fast, local object detector that spots missing hardhats, vests, and harnesses in 1.6 ms per frame with pixel‑perfect bounding boxes and zero hallucination risk. The “brain” is Google Gemini 2.5, which wakes only when YOLO finds a potential violation. A Vision Agent confirms the threat; a Regulation Agent pulls the verbatim OSHA standard from an 800‑page PDF loaded in context; and an Alert Agent drafts a structured, legally‑grounded supervisor notification. Crucially, no alarm sounds until Gemini has verified the danger—giving the supervisor a yellow “checking” card first, then a red verified alert with the exact OSHA code and penalty, while the worker receives an instant polite audio reminder. TECHNICAL HIGHLIGHTS • YOLOv8 + Roboflow PPE model – 1.6 ms per frame, 30+ FPS, local inference • Gemini 2.5 Flash (vision confirmation), Pro (OSHA grounding), Flash‑Lite (alert formatting) • Python FastAPI async backend with WebSocket push to a React dashboard • SQLite audit log records every detection, verification, and human acknowledgment • Human‑in‑the‑loop approval – the supervisor always has the final say BUSINESS VALUE SafeSite AI reduces preventable injuries and OSHA fines. Every avoided fall saves an estimated $145 000 in penalties and litigation, while letting supervisors focus on high‑value tasks instead of monitoring screens.

Belto - Onboard AI for satellites

Earth-observation satellites today downlink almost everything they capture and let the ground sort it out. Clouds, ocean, empty desert — it all consumes bandwidth. BELTO inverts that. Our SDK is four lines of code a satellite builder drops into their flight software. Once it's in, the satellite classifies every frame onboard, runs it through a rule engine, and decides what's worth sending home. To prove it works, we built a browser-deployed demo: an 18-satellite fleet dashboard with a real ResNet-18 scene classifier trained on Sentinel-2 imagery. The model downloads into your browser and runs on your laptop — not on a server. Feed it a MODIS tile of a wildfire and the rule engine fires PRIORITY_FIRE at CRITICAL severity in real time. Eight of the eighteen satellites fly on live orbits pulled from Celestrak, so you see the actual ISS, Hubble, and Landsat-9 where they are right now.