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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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