Datasets:

whirlwind-ams
/

hsl-objects-v1

dataset_info:
  features:
    - name: image_id
      dtype: int64
    - name: image
      dtype: image
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: objects
      struct:
        - name: area
          list: float32
        - name: bbox
          list:
            list: float32
            length: 4
        - name: category
          list:
            class_label:
              names:
                '0': Ball
                '1': Goalpost
                '2': K1
                '3': L-Intersections
                '4': Penalty Marks
                '5': T-Intersections
                '6': X-Intersections
        - name: id
          list: int64
  splits:
    - name: train
      num_bytes: 4072008285
      num_examples: 4029
    - name: validation
      num_bytes: 507232886
      num_examples: 504
    - name: test
      num_bytes: 517076536
      num_examples: 504
  download_size: 5096513815
  dataset_size: 5096317707
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - object-detection
tags:
  - robocup
  - robotics
  - soccer
size_categories:
  - 1K<n<10K
license: mit

HSL Objects v1

5,037 images with COCO-style bounding box annotations for object detection in the RoboCup Humanoid Soccer League (HSL). Split 80/10/10 into train (4,029), validation (504), and test (504).

Classes

ID	Class
1	Ball
2	Goalpost
3	K1
4	L-Intersections
5	Penalty Marks
6	T-Intersections
7	X-Intersections

Sample Structure

Each example contains:

image_id (int64) -- unique image identifier
image (Image) -- RGB image
width / height (int64) -- image dimensions in pixels
objects -- annotation dictionary:
- id (list[int64]) -- object instance IDs
- category (list[int64]) -- category IDs (1--7)
- bbox (list[list[float64]]) -- bounding boxes in COCO format [x, y, width, height]
- area (list[float64]) -- bounding box areas in pixels

Sources

RoboCup Asia Pacific Beijing Masters 2025
RoboCup Salvador 2025
RoboCup German Open 2025
RoboCup German Open 2026
Intelligent Robotics Lab, University of Amsterdam

Usage

from datasets import load_dataset
ds = load_dataset("whirlwind-ams/hsl-objects-v1")

Citation

If you use this dataset, please cite:

@misc{hsl_objects_v1,
  title={HSL Objects v1},
  author={Whirlwind Amsterdam},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/whirlwind-ams/hsl-objects-v1}}
}