YOLO

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

YOLO

YOLO (You Only Look Once) is a state-of-the-art, real-time object detection algorithm that can quickly detect and locate objects within an image or video. The YOLO architecture works by taking an input and separating it into a grid of cells and each of these cells is in charge of detecting objects within that region. YOLO returns the bounding boxes containing all the objects in the image and predicts the probability of an object being in each of the boxes and also predicts a class probability to help identify the type of object it is. YOLO is a highly effective object detection algorithm and making YOLO and open-source project led the community to make several improvements in such a limited time.

General
Relese date	2015
Author	Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
Paper	(https://arxiv.org/abs/1506.02640)
Type	Object detection algorithm

YOLO - Resources

Learn even more about YOLO!

v7 Labs Blog "YOLO: Algorithm for Object Detection Explained".
YOLOv5 Repository Object detection architectures and models pretrained on the COCO dataset.
YOLOv6 Web demo Gradio demo for YOLOv6 for object detection on videos.
Hugging Face Spaces Test YOLOv7 in the browser with Hugging Face Spaces.

Edit on GitHub

YOLO AI Technologies Hackathon projects

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

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.