README.md

metadata

license: apache-2.0
pretty_name: Keypoint Detection Demo
task_categories:
  - keypoint-detection
tags:
  - computer-vision
  - pictograph
annotations_creators:
  - expert-generated
size_categories:
  - n<1K
source_datasets:
  - original
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/*

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.

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Published from Pictograph - annotate, train, and deploy from one API.