Datasets:

weslien
/

topspin-opentt-ball-subset

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 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, 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, 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):

Seek the source mp4 to that frame index.
JPEG-encode the decoded frame (quality 95).
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.