| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - table-tennis |
| - sports |
| - ball-detection |
| - yolo |
| - yolov8 |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "images/game_*/*.jpg" |
| - split: test |
| path: "images/test_*/*.jpg" |
| --- |
| |
| # OpenTTGames ball-detection YOLOv8 subset |
|
|
| A YOLOv8-format training subset extracted from the |
| [OpenTTGames](https://lab.osai.ai/) dataset. Every source-video frame for |
| which OpenTTGames provides a ball-centre annotation is included as a |
| single JPEG + a single-row YOLO label (class `0` = ball). |
|
|
| > **Dataset viewer note.** The viewer above only renders the JPEG |
| > images. Per-frame YOLO bounding-box labels live alongside each image |
| > at `labels/<match_id>/<frame_index>.txt`, one label file per image, |
| > paired by filename. The viewer doesn't render the bbox overlays. |
| > Consume the dataset via Ultralytics / your training pipeline of choice |
| > to get image + label pairs. |
|
|
| ## Source |
|
|
| Derivative work of [OpenTTGames](https://lab.osai.ai/), the table-tennis |
| perception dataset by OSAI. The original dataset is described in: |
|
|
| > Voeikov, R., Falaleev, N., & Baikulov, R. (2020). *TTNet: Real-time |
| > temporal and spatial video analysis of table tennis*. arXiv:2004.09927. |
| > https://arxiv.org/abs/2004.09927 |
|
|
| Original archives at `https://lab.osai.ai/datasets/openttgames/data/` — |
| `<match>.mp4` + `<match>.zip` siblings, twelve matches total |
| (`game_1`–`game_5` train, `test_1`–`test_7` test). |
|
|
| ## License |
|
|
| This subset is licensed under |
| [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), |
| matching the source dataset's license. By the ShareAlike clause, any |
| further derivatives must also be licensed under CC BY-NC-SA 4.0 or a |
| compatible license. **NonCommercial use only.** |
|
|
| Attribution: OSAI, https://lab.osai.ai/ — see citation above. See the |
| `LICENSE` file in this repo for the full text reference. |
|
|
| ## Extraction recipe |
|
|
| For every frame whose integer index appears as a key in the match's |
| `ball_markup.json` (i.e. every frame OpenTTGames has annotated with a |
| ball-centre point): |
|
|
| 1. Seek the source mp4 to that frame index. |
| 2. JPEG-encode the decoded frame (quality 95). |
| 3. Emit a YOLO label with a single row: class `0` (ball), centre at the |
| annotated `(x, y)` pixel coordinate, bounding box a fixed 32 px |
| square (16 px half-size), normalised against the frame dimensions. |
|
|
| Frames absent from `ball_markup.json` (ball occluded, out of frame, or |
| not annotated) are not included. |
|
|
| ## Layout |
|
|
| ``` |
| images/<match_id>/<frame_index>.jpg |
| labels/<match_id>/<frame_index>.txt |
| data.yaml |
| README.md |
| LICENSE |
| ``` |
|
|
| Where `<match_id>` is one of `game_1`..`game_5` (train) or |
| `test_1`..`test_7` (test). |
|
|
| ## Class map |
|
|
| | id | name | |
| |----|------| |
| | 0 | ball | |
|
|
| ## Intended use |
|
|
| Research and non-commercial single-class ball-detection model training. |
| No commercial use. |
|
|
