Registry indexed
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
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.
Source documentation, not instructions for this website. Review permissions before running any commands.
The #1 cause of silent training failure is a malformed dataset — validate before training.
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 use its URI directly:
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.
images → labels mirror ruleUltralytics 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, are not found → silent
all-background training.path: /abs/dataset/root # relative paths resolve against `yolo settings` datasets_dir — prefer absolute
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 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
dataset/train/<class>/*.jpg, dataset/val/<class>/*.jpg); train with
data=path/to/dataset.label-formats.md (this folder)
whenever writing or debugging label files.from ultralytics.data.converter import convert_coco
convert_coco(labels_dir="coco/annotations/", use_segments=True) # COCO → detect/segment
convert_coco(labels_dir="coco/annotations/", use_keypoints=True) # COCO → pose
Also in ultralytics.data.converter: convert_dota_to_yolo_obb(root) (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="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt")
For VOC XML/CSV there is no converter — write a small script emitting the per-task line format (normalize coords, center-based boxes).
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).
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.
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.
---
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. For detect, segment, semantic, and OBB, switch to
**Smart** mode to label with SAM or predictions from a compatible official/custom YOLO
model.
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 use its URI directly:
```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, or in a dir not named `labels`, are **not found** → silent
all-background training.
- 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 # relative paths resolve against `yolo settings` datasets_dir — prefer absolute
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 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) # COCO → detect/segment
convert_coco(labels_dir="coco/annotations/", use_keypoints=True) # COCO → pose
```
Also in `ultralytics.data.converter`: `convert_dota_to_yolo_obb(root)` (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="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt")
```
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.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: AGPL-3.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
55/100
Promising
Trust
58/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"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"
}
}Listing source
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Do not auto-install
Audit
71/100
Risky
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.