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Keypoint Detection Demo
60 images and 360 annotations across 6 classes, annotated as keypoint.
View on Pictograph · Pictograph · Apache License 2.0
About
Keypoint Detection Demo is a computer-vision dataset curated and annotated on Pictograph. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.
At a glance
| Metric | Value |
|---|---|
| Images | 60 |
| Annotations | 360 |
| Classes | 6 |
| Annotation types | keypoint |
| Splits | train |
Dataset structure
This dataset uses the Hugging Face imagefolder layout: each split directory holds the images plus a metadata.jsonl that links every image to its annotations by file_name.
| Field | Description |
|---|---|
file_name |
Path to the image within the split directory. |
objects.bbox |
Bounding boxes as [x, y, width, height] (pixels). |
objects.categories |
Integer class index per box (matches the class list below). |
objects.category_names |
Human class name per box. |
keypoints |
Keypoint instances (COCO-pose): object category plus a flat [x, y, v, ...] array aligned to node_names. |
Use it
from datasets import load_dataset
ds = load_dataset("pictograph/keypoint-detection-demo")
print(ds)
Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? Open this dataset on Pictograph.
Classes
Class index matches objects.categories in metadata.jsonl.
| # | Class | Annotations |
|---|---|---|
| 0 | head | 60 |
| 1 | torso | 60 |
| 2 | l_hand | 60 |
| 3 | r_hand | 60 |
| 4 | l_foot | 60 |
| 5 | r_foot | 60 |
License
Released under Apache License 2.0.
Published from Pictograph - annotate, train, and deploy from one API.
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