Datasets:
license: mit
task_categories:
- object-detection
tags:
- robotics
- synthetic
- robocup
pretty_name: NUpbr Ball Detection & Description
NUpbr Ball Detection & Description Dataset
Synthetic renders of soccer balls (from the NUpbr generator) with bounding boxes and free-text visual descriptions, for training semantic ball detectors that generalise to ball appearances not seen during training.
Dataset structure
train/metadata.csv # 1440 images, 72 ball instances
train/*.png
validation/metadata.csv # 320 images, 16 ball instances
validation/*.png
test/metadata.csv # 300 images, 15 ball instances
test/*.png
meta/*.yaml # per-frame raw render annotation (provenance), one per image, matched by filename stem
ball_descriptions.csv # one row per ball instance (103 rows): Ball Name, Description
Loads directly with:
from datasets import load_dataset
ds = load_dataset("imagefolder", data_dir=".")
metadata.csv columns
| column | meaning |
|---|---|
file_name |
image filename, relative to the split folder |
width, height |
image dimensions in pixels |
x1, y1, x2, y2 |
ball bounding box in pixels |
class_name |
always ball |
instance_id |
which of the 103 distinct ball instances appears in this image (e.g. ball_000) |
description |
free-text visual description of this ball instance, joined in from ball_descriptions.csv |
meta_file |
filename of the matching raw per-frame annotation in meta/ |
Split
The split is by ball instance, not by image: every image of a given ball instance lives entirely in one split. This means validation and test measure generalisation to ball appearances/descriptions the model never saw during training, not just held-out camera angles of a known ball.
| split | images | instances |
|---|---|---|
| train | 1440 (69.9%) | 72 |
| validation | 320 (15.5%) | 16 |
| test | 300 (14.6%) | 15 |
Each ball instance contributes exactly 20 images across its assigned split.
Notes
- Filenames were renumbered sequentially (
0000.png...2059.png) across the whole dataset.
License and acknowledgments
Released under the MIT license (see LICENSE). Images were rendered with
NUpbr, a synthetic data generator built by the NUbots RoboCup team at the University of Newcastle, Australia; this dataset and the descriptions/annotations were produced independently by Ysobel Sims.