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How to label images and train a YOLO model on your own machine with Yolo Web Agent Starter

Annotate detect, segment, OBB, or classify data in a browser and start a local training job.

Who it is forA computer vision engineer with a folder of photos who needs a first custom YOLO model and wants all data kept on a local machine.

The problemPreparing YOLO data by hand means juggling label files, splits, and training scripts across several tools.

The outcomeA validated dataset with a persisted train/val/test split, exported YOLO data, and best.pt and last.pt registered in the managed model library.

What you need first

  • 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

Steps

  1. 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.
  2. Start both services with ./run-all.sh and open the web app in a browser.
  3. Create a detect, segment, obb, or classify dataset, upload images, or place images under data/imports/ and scan them in the interface.
  4. Add classes and annotate with bbox, polygon, OBB, or a single classify label per image.
  5. Make sure train and val each have at least one annotated image, run dataset validation, then export or import YOLO data.
  6. 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.

Prompt for your agent

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.

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.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for fengzhizi715/YoloWebAgentStarter and "Label images and train a YOLO model on your own machine"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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