yolo-tuning skillA
yolo-tuning is agent-read markdown (skill) from ultralytics/skills: Use when improving or comparing Ultralytics YOLO models in Platform or code, or running hyperparameter search/autotraining — Platform experiment comparison, the systematic improvement playbook, model.tune() genetic evolution, Ray Tune, search spaces, and deciding whether tuning is worthwhile. For one training run and its arguments, see yolo-training..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Improving models & hyperparameter tuning ## The improvement playbook (follow in order — tuning is the LAST step) Hyperparameter tuning is expensive and usually not the bottleneck. Escalate in this order, re-validating after each step: 1. **Fix the data** — check `confusion_matrix.png` and `train_batch*.jpg` for label noise; review the top false-negative/false-positive val images; add examples of failing classes and true-background images. Data quality beats every other lever. 2. **Train longer** — if val mAP was still rising at the end: more `epochs`, higher `patience`. 3. **Bigger input** — small objects or mAP50 ≫ mAP50-95: raise `imgsz` (640 → 960/1280). 4. **Bigger model** — underfitting (train and val both mediocre): n → s → m → l. 5. **Domain-matched augmentation** — aerial `degrees=180 flipud=0.5`, crowded scenes `copy_paste=0.3`/`mixup=0.1`, color-critical classes lower `hsv_h` (see yolo-training's `training-args.md`). 6. **Only now: hyperparameter tuning** — worth ~0.5–2 mAP when everything above is exhausted. Decision signals: overfitting (val drops while train improves) → more data/aug or …
Read the whole file at its exact version.
How to install
mdr add ultralytics/skills/yolo-tuning@git:20260820.dcee0dbmdr add ultralytics/skills/yolo-tuning@sha256:c29ed67cdfb35669Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_s6hvrnpiujd5qyfj)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260820.dcee0db latest | 2026-08-20 | dcee0db | 5,209 B | A | view · diff |
| git:20260811.e35dbcc | 2026-08-11 | e35dbcc | 5,210 B | A | view · diff |
| git:20260811.47343d9 | 2026-08-11 | 47343d9 | 4,560 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (5209 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ultralytics/skills · 25 stars · license AGPL-3.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_s6hvrnpiujd5qyfj GET https://markdownregistry.com/api/v1/resolve?ref=ultralytics/skills/yolo-tuning GET https://markdownregistry.com/api/v1/blob/c29ed67cdfb356692c053a4310e82ae115c9bfd7c5325f27964fd94c62cc8fef
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