{
 "schema_version": 1,
 "generated": "2026-10-24",
 "id": "fengzhizi715-yolowebagentstarter--label-and-train-yolo-dataset-locally",
 "repo": "fengzhizi715/YoloWebAgentStarter",
 "repo_url": "https://github.com/fengzhizi715/YoloWebAgentStarter",
 "repo_slug": "fengzhizi715-yolowebagentstarter",
 "title": "Label images and train a YOLO model on your own machine",
 "page_title": "How to label images and train a YOLO model on your own machine with Yolo Web Agent Starter",
 "summary": "Annotate detect, segment, OBB, or classify data in a browser and start a local training job.",
 "who": "A computer vision engineer with a folder of photos who needs a first custom YOLO model and wants all data kept on a local machine.",
 "problem": "Preparing YOLO data by hand means juggling label files, splits, and training scripts across several tools.",
 "outcome": "A validated dataset with a persisted train/val/test split, exported YOLO data, and best.pt and last.pt registered in the managed model library.",
 "steps": [
  "Run `python3.12 -m venv .venv`, then `.venv/bin/pip install -r backend/requirements.txt -r backend/requirements-dev.txt`, then `npm --prefix frontend install`.",
  "Start both services with `./run-all.sh` and open the web app in a browser.",
  "Create a detect, segment, obb, or classify dataset, upload images, or place images under data/imports/ and scan them in the interface.",
  "Add classes and annotate with bbox, polygon, OBB, or a single classify label per image.",
  "Make sure train and val each have at least one annotated image, run dataset validation, then export or import YOLO data.",
  "On the training page, pick a matching YOLOv8, YOLO11, or YOLO26 base weight and start a local job, then check the managed best.pt and last.pt in the model workspace."
 ],
 "agent_prompt": "Using the YoloWebAgentStarter repo at <repo path>, set up the local workbench and create a detect dataset from the images in <image folder>. Confirm that validation passes and that the dataset has annotated images in both train and val. Report the dataset ID, the validation summary, and any blocking errors.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "Named base weights may be downloaded by Ultralytics on first use, so confirm the license before using them, and do not commit weights, datasets, or outputs.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "macOS or Linux",
  "Python 3.11 or 3.12",
  "Node.js 20 or newer",
  "A set of images to label, or the built-in tiny demo generator"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/label-and-train-yolo-dataset-locally/",
 "md": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/label-and-train-yolo-dataset-locally/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/fengzhizi715-yolowebagentstarter--label-and-train-yolo-dataset-locally.json",
 "api_version": "v1"
}