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yolo-datasets
Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and e
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Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
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Ultralytics YOLO datasets
The #1 cause of silent training failure is a malformed dataset — validate before training.
Fastest route: prepare data in Platform
- Open Platform, create a dataset under Annotate, and choose its task.
- Upload images, videos, ZIP/TAR archives, or NDJSON. Existing YOLO labels and COCO JSON can be imported; cloud-storage integrations can keep supported data in place.
- Open an image in the fullscreen editor. Use manual tools for detect, segment, semantic, classify, pose, or OBB. Switch to Smart mode to label with SAM (detect, segment, semantic, OBB) or a compatible official/custom YOLO model (those tasks plus pose).
- Review the Classes, Charts, and Errors tabs, fix the split, and create a numbered dataset version before important runs.
Platform datasets cover all seven YOLO tasks. Depth targets are imported rather than drawn in the editor. See Platform Data and the Annotation Editor.
To train on the same dataset from local code, create an API key under Settings > API Keys, set ULTRALYTICS_API_KEY, and pass the dataset's ul:// URI as data:
yolo train model=yolo26n.pt data=ul://username/datasets/dataset-slug epochs=100
Export NDJSON when you need a portable snapshot instead of live Platform access.
The images → labels mirror rule
Ultralytics finds a label file by replacing the last /images/ path segment with /labels/ and the image extension with .txt:
dataset/
├── data.yaml
├── images/train/ img001.jpg ... ├── images/val/ ...
└── labels/train/ img001.txt ... └── labels/val/ ...
- Labels next to images in an
images/dir, or in a dir not namedlabels, are not found → training stops withNo labels found; if only some are missing, those images train as backgrounds. - Filenames must match stems exactly (case-sensitive on Linux).
- An image with no/empty label file trains as a background image. A few percent of true backgrounds reduce false positives; accidentally missing labels destroy recall.
data.yaml anatomy
path: /abs/dataset/root # prefer absolute; a relative path missing from the CWD resolves under datasets_dir
train: images/train # dir, .txt file of image paths, or list of dirs
val: images/val
test: images/test # optional
names: # 0-based, contiguous indices
0: person
1: helmet
# pose only:
kpt_shape: [17, 3] # [num_keypoints, dims]; dims 2 (x,y) or 3 (x,y,visibility)
flip_idx: [0, 2, 1, ...] # L/R keypoint swap map — without it, flip augs are auto-disabled
# semantic only (optional — polygon .txt labels in labels/ also work):
masks_dir: masks # per-pixel PNG mask images
# depth only (replace names above; pair depth/{train,val}/*.png or float .npy maps):
# nc: 1
# names: {0: depth}
# depth_scale: 1000 # PNG units per meter; default millimeters
- Classification datasets use no yaml: folder structure is the label (
dataset/train/<class>/*.jpg,dataset/val/<class>/*.jpg); train withdata=path/to/dataset. - Per-task label line formats and their gotchas: read
label-formats.md(this folder) whenever writing or debugging label files.
Converting from other formats — use built-ins first
from ultralytics.data.converter import convert_coco
convert_coco(labels_dir="coco/annotations/", use_segments=True, cls91to80=False) # → segment
convert_coco(labels_dir="coco/annotations/", use_keypoints=True, cls91to80=False) # → pose
Omit use_segments for detect labels. cls91to80=False keeps custom category IDs (as category_id - 1); leave the default True only for the official COCO 91-class IDs.
Also in ultralytics.data.converter: convert_dota_to_yolo_obb(dota_root_path) (DOTA → OBB), convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes) (index PNGs → polygons), yolo_bbox2segment(im_dir) (upgrade detect labels to segment via SAM), convert_to_multispectral(path, n_channels).
Auto-label a raw image folder (detector proposes boxes, SAM refines masks):
from ultralytics.data.annotator import auto_annotate
auto_annotate(
data="dataset/images/train", det_model="yolo26x.pt", sam_model="sam_b.pt", output_dir="dataset/labels/train"
)
For VOC XML/CSV there is no converter — write a small script emitting the per-task line format (normalize coords, center-based boxes).
Splitting
from ultralytics.data.split import autosplit
autosplit(path="dataset/images", weights=(0.9, 0.1, 0.0)) # writes autosplit_*.txt lists
Point train:/val: at the generated .txt files. Keep frames from the same video/scene in ONE split — per-image random splits of video frames leak near-duplicates into val and inflate mAP. For classify: split_classify_dataset(source_dir, 0.8).
Validate before training (use the task-relevant steps in order)
-
YAML/path check (non-classification) — validates required fields, resolves configured paths, checks the requested split (default
val), and may auto-download a known dataset. It does not count or parse individual images and targets:from ultralytics.data.utils import check_det_dataset check_det_dataset("data.yaml") # detect/segment/pose/obb/semantic/depth -
Detection-only visual spot check — this helper accepts five-column
class cx cy w hrows; wrong normalization or swapped axes become visible:from ultralytics.data.utils import visualize_image_annotations visualize_image_annotations( "images/train/img001.jpg", "labels/train/img001.txt", label_map={0: "person", 1: "helmet"}, )Do not use this helper for segment, pose, OBB, semantic, depth, or classify targets; use their task-aware trainer plots instead.
-
Task-loader smoke test — run one epoch with matching task/model/data; this builds the real dataset and produces task-aware training plots (detection example):
yolo detect train data=data.yaml model=yolo26n.pt epochs=1 fraction=0.1 # inspect runs/detect/train/train_batch0.jpg — boxes must sit on objects -
Distribution sanity — check task-appropriate class/target balance and split leakage. For box tasks, also inspect very small boxes at train
imgszand the background-image share.
Known-good tiny datasets for pipeline smoke tests (auto-download): coco8.yaml, coco8-seg.yaml, coco8-pose.yaml, dota8.yaml, cityscapes8.yaml, depth8.yaml, imagenet10.
Related pages
label-formats.md— exact per-task label line formats + symptom→cause table. Read when writing labels, converting formats, or any label-related error.
If the installed version rejects an argument or format here, trust its error message and yolo checks over this file.
Métadonnées du fichier
name: yolo-datasets description: > Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
Voir le texte original
---
name: yolo-datasets
description: >
Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
---
# Ultralytics YOLO datasets
The #1 cause of silent training failure is a malformed dataset — validate before training.
## Fastest route: prepare data in Platform
1. Open [Platform](https://platform.ultralytics.com), create a dataset under **Annotate**, and choose its task.
2. Upload images, videos, ZIP/TAR archives, or NDJSON. Existing YOLO labels and COCO JSON can be imported; cloud-storage integrations can keep supported data in place.
3. Open an image in the fullscreen editor. Use manual tools for detect, segment, semantic, classify, pose, or OBB. Switch to **Smart** mode to label with SAM (detect, segment, semantic, OBB) or a compatible official/custom YOLO model (those tasks plus pose).
4. Review the **Classes**, **Charts**, and **Errors** tabs, fix the split, and create a numbered dataset version before important runs.
Platform datasets cover all seven YOLO tasks. Depth targets are imported rather than drawn in the editor. See [Platform Data](https://docs.ultralytics.com/platform/data) and the [Annotation Editor](https://docs.ultralytics.com/platform/data/annotation).
To train on the same dataset from local code, create an API key under **Settings > API Keys**, set `ULTRALYTICS_API_KEY`, and pass the dataset's `ul://` URI as `data`:
```bash
yolo train model=yolo26n.pt data=ul://username/datasets/dataset-slug epochs=100
```
Export NDJSON when you need a portable snapshot instead of live Platform access.
## The `images` → `labels` mirror rule
Ultralytics finds a label file by replacing the **last** `/images/` path segment with `/labels/` and the image extension with `.txt`:
```
dataset/
├── data.yaml
├── images/train/ img001.jpg ... ├── images/val/ ...
└── labels/train/ img001.txt ... └── labels/val/ ...
```
- Labels next to images in an `images/` dir, or in a dir not named `labels`, are **not found** → training stops with `No labels found`; if only some are missing, those images train as backgrounds.
- Filenames must match stems exactly (case-sensitive on Linux).
- An image with no/empty label file trains as a **background image**. A few percent of true backgrounds reduce false positives; accidentally missing labels destroy recall.
## data.yaml anatomy
```yaml
path: /abs/dataset/root # prefer absolute; a relative path missing from the CWD resolves under datasets_dir
train: images/train # dir, .txt file of image paths, or list of dirs
val: images/val
test: images/test # optional
names: # 0-based, contiguous indices
0: person
1: helmet
# pose only:
kpt_shape: [17, 3] # [num_keypoints, dims]; dims 2 (x,y) or 3 (x,y,visibility)
flip_idx: [0, 2, 1, ...] # L/R keypoint swap map — without it, flip augs are auto-disabled
# semantic only (optional — polygon .txt labels in labels/ also work):
masks_dir: masks # per-pixel PNG mask images
# depth only (replace names above; pair depth/{train,val}/*.png or float .npy maps):
# nc: 1
# names: {0: depth}
# depth_scale: 1000 # PNG units per meter; default millimeters
```
- Classification datasets use **no yaml**: folder structure is the label (`dataset/train/<class>/*.jpg`, `dataset/val/<class>/*.jpg`); train with `data=path/to/dataset`.
- Per-task label line formats and their gotchas: read `label-formats.md` (this folder) whenever writing or debugging label files.
## Converting from other formats — use built-ins first
```python
from ultralytics.data.converter import convert_coco
convert_coco(labels_dir="coco/annotations/", use_segments=True, cls91to80=False) # → segment
convert_coco(labels_dir="coco/annotations/", use_keypoints=True, cls91to80=False) # → pose
```
Omit `use_segments` for detect labels. `cls91to80=False` keeps custom category IDs (as `category_id - 1`); leave the default `True` only for the official COCO 91-class IDs.
Also in `ultralytics.data.converter`: `convert_dota_to_yolo_obb(dota_root_path)` (DOTA → OBB), `convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes)` (index PNGs → polygons), `yolo_bbox2segment(im_dir)` (upgrade detect labels to segment via SAM), `convert_to_multispectral(path, n_channels)`.
Auto-label a raw image folder (detector proposes boxes, SAM refines masks):
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(
data="dataset/images/train", det_model="yolo26x.pt", sam_model="sam_b.pt", output_dir="dataset/labels/train"
)
```
For VOC XML/CSV there is no converter — write a small script emitting the per-task line format (normalize coords, center-based boxes).
## Splitting
```python
from ultralytics.data.split import autosplit
autosplit(path="dataset/images", weights=(0.9, 0.1, 0.0)) # writes autosplit_*.txt lists
```
Point `train:`/`val:` at the generated `.txt` files. Keep frames from the same video/scene in ONE split — per-image random splits of video frames leak near-duplicates into val and inflate mAP. For classify: `split_classify_dataset(source_dir, 0.8)`.
## Validate before training (use the task-relevant steps in order)
1. **YAML/path check (non-classification)** — validates required fields, resolves configured paths, checks the requested split (default `val`), and may auto-download a known dataset. It does not count or parse individual images and targets:
```python
from ultralytics.data.utils import check_det_dataset
check_det_dataset("data.yaml") # detect/segment/pose/obb/semantic/depth
```
2. **Detection-only visual spot check** — this helper accepts five-column `class cx cy w h` rows; wrong normalization or swapped axes become visible:
```python
from ultralytics.data.utils import visualize_image_annotations
visualize_image_annotations(
"images/train/img001.jpg",
"labels/train/img001.txt",
label_map={0: "person", 1: "helmet"},
)
```
Do not use this helper for segment, pose, OBB, semantic, depth, or classify targets; use their task-aware trainer plots instead.
3. **Task-loader smoke test** — run one epoch with matching task/model/data; this builds the real dataset and produces task-aware training plots (detection example):
```bash
yolo detect train data=data.yaml model=yolo26n.pt epochs=1 fraction=0.1
# inspect runs/detect/train/train_batch0.jpg — boxes must sit on objects
```
4. **Distribution sanity** — check task-appropriate class/target balance and split leakage. For box tasks, also inspect very small boxes at train `imgsz` and the background-image share.
Known-good tiny datasets for pipeline smoke tests (auto-download): `coco8.yaml`, `coco8-seg.yaml`, `coco8-pose.yaml`, `dota8.yaml`, `cityscapes8.yaml`, `depth8.yaml`, `imagenet10`.
## Related pages
- `label-formats.md` — exact per-task label line formats + symptom→cause table. Read when writing labels, converting formats, or any label-related error.
If the installed version rejects an argument or format here, trust its error message and `yolo checks` over this file.
Examiner la source
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- AGPL-3.0
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Source à réexaminer
La source a changé ou sa synchronisation a échoué. Vérifiez-la avant installation.
Réviser avant installation: Éviter l’installation automatique
Licence: AGPL-3.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- L’approbation de revue IA est absente
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 28 GitHub stars
- Stars/forks activity: 28 stars, 0 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.
Commencer par une petite tâche
- 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
- 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
- 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.
Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.
Source et conseils d’utilisation
Répertorié
Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.
- Dépôt source
- ultralytics/skills
- Licence
- AGPL-3.0
- Version
- Unknown
- Dernier push GitHub
- 6 oct. 2026
- Registre mis à jour
- 7 oct. 2026
- Chemin des instructions
- skills/yolo-datasets/SKILL.md @ acfe53ce376f
Version déclarée dans le registre ; vérifiez les versions de la source.
Qualité
56/100
Prometteur
Confiance
61/100
Sandbox uniquement
Audit
72/100
Risqué
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- L’approbation de revue IA est absente
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 28 GitHub stars
- Stars/forks activity: 28 stars, 0 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
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- —
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Plus de détails
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"agent_contract": {
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"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 69/100 Manual review",
"Audit: 72/100 Risky",
"Safety: 32/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "ultralytics-yolo-datasets (yolo-datasets)",
"install_command": "",
"risk_summary": "Risky; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "ultralytics-yolo-datasets",
"task": "Use yolo-datasets in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/ultralytics-yolo-datasets",
"api": "https://www.openagentskill.com/api/agent/skills/ultralytics-yolo-datasets",
"audit": "https://www.openagentskill.com/skills/ultralytics-yolo-datasets/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=ultralytics-yolo-datasets&task=Use%20yolo-datasets%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20yolo-datasets%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20yolo-datasets%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/ultralytics-yolo-datasets/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/ultralytics-yolo-datasets"
}
}Pour le créateur
Source de la fiche
Indexé par Registry
Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.
- Créateur
- ultralytics
- Source
- ultralytics/skills
- Indexé par
- Index communautaire OpenAgentSkill
L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.
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