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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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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 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

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

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 h rows; 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 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.

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.

查看来源 ↗

文件元数据

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.

查看原始文本

---
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.

查看并核实来源

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许可证: 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
缺少 AI 审查批准
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

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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: ultralytics/skills
许可证: AGPL-3.0
版本: Unknown
最近 GitHub 推送: 2026年10月6日
目录更新于: 2026年10月7日
技能指令路径: skills/yolo-datasets/SKILL.md @ acfe53ce376f

版本来自目录元数据，使用前请核实来源发布记录。

质量

56/100

有潜力

信任

61/100

仅限沙盒

审计

72/100

高风险

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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
缺少 AI 审查批准
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

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      "Low GitHub adoption signal",
      "AI review approval is missing",
      "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"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 56,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "6d since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "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"
  ],
  "agent_contract": {
    "task_input": "Use yolo-datasets in an agent workflow",
    "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"
  }
}

创作者工具

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此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: ultralytics
来源: ultralytics/skills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 ultralytics，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/ultralytics-yolo-datasets?metric=listed&label=Listed)](https://www.openagentskill.com/skills/ultralytics-yolo-datasets?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/ultralytics-yolo-datasets?metric=trust&label=Trust)](https://www.openagentskill.com/skills/ultralytics-yolo-datasets?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/ultralytics-yolo-datasets?metric=audit&label=Audit)](https://www.openagentskill.com/skills/ultralytics-yolo-datasets/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/ultralytics-yolo-datasets?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/ultralytics-yolo-datasets?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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