---
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.