| --- |
| 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. |
|
|
