{
 "schema_version": 1,
 "generated": "2026-10-24",
 "id": "fengzhizi715-yolowebagentstarter--evaluate-model-and-review-error-samples",
 "repo": "fengzhizi715/YoloWebAgentStarter",
 "repo_url": "https://github.com/fengzhizi715/YoloWebAgentStarter",
 "repo_slug": "fengzhizi715-yolowebagentstarter",
 "title": "Score a trained model on a held-out split and inspect its mistakes",
 "page_title": "How to score a trained model on a held-out split and inspect its mistakes with Yolo Web Agent Starter",
 "summary": "Run native YOLO val on a managed PT model and review up to 200 saved error samples.",
 "who": "A model developer who has a trained checkpoint and needs honest metrics on the validation or test split before deciding to use it.",
 "problem": "A single mAP number hides where the model fails, and rerunning evaluation by hand loses context.",
 "outcome": "Saved metrics, logs, confusion matrix, PR curves when generated, and a list of missed detections, false positives, and low-confidence samples to review.",
 "steps": [
  "Start the workbench with `./run-all.sh`.",
  "Open the model workspace and pick a managed PT model produced by a training run.",
  "Create an evaluation task on the train, val, or test split and wait for the background job to finish.",
  "Open the evaluation details to read the metrics, confusion matrix, and charts, then review the error samples.",
  "Compare results across runs only on the same dataset, since the README notes that a data snapshot fingerprint is not yet verifiable."
 ],
 "agent_prompt": "In the YoloWebAgentStarter repo at <repo path>, run a background evaluation of managed model <model ID> on the test split of dataset <dataset ID>. Verify that the metrics, logs, and error sample list are saved and at most 200 entries. Return the box metrics and the count of each error type.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "Chart files only appear when Ultralytics generates them, so a tiny random model may produce metrics and logs without any PR curve.",
 "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": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/evaluate-model-and-review-error-samples/",
 "md": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/evaluate-model-and-review-error-samples/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--evaluate-model-and-review-error-samples.json",
 "api_version": "v1"
}