Datasets:
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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](https://github.com/NUbots/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:
```python
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](https://github.com/NUbots/NUpbr), a synthetic data generator built by the [NUbots](https://nubots.newcastle.edu.au/) RoboCup team at the University of Newcastle, Australia; this dataset and the descriptions/annotations were produced independently by Ysobel Sims.
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