Datasets:
Request access to the OpenSportsBall benchmark data
This dataset contains video of real, identifiable adult players in soccer games. A maintainer reviews each request by hand, usually within a few days.
By requesting access you agree to these terms. Use the data only to develop or evaluate sports video processing, such as ball, player and pitch detection, camera calibration and tracking. Don't redistribute it or any frames from it, and don't publish frames in which a person can be recognised. Don't try to identify the people in it, and don't use it for face recognition, biometrics or surveillance. Keep your copy access-controlled and delete it when you stop contributing or when the maintainers ask. Access can be revoked at any time.
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OpenSportsBall benchmark data
The data behind the benchmarks of OpenSportsBall, an open-source system that films amateur field sports with two phones and renders a broadcast-style video. It holds the recorded video and the labels the processing pipeline is scored against, so contributors can rerun every benchmark and compare their changes against the same numbers.
Access is gated: the footage shows real adult players who can be identified, so it is shared only with contributors who accept the terms above.
Contents
The repository mirrors the footage/ directory of
opensportsball-processing, so a download lands where the tools already look.
| Path | What |
|---|---|
bench/rec3/, bench/rec4cal/ |
The bench store for two night games: the phones' video segments the benches read (unchanged, sha256-checked against each manifest), plus sync, pitch maps, team colours, background models, play-box sets, ball label sets and detector caches |
bench/fixtures/pitch/ |
Pitch-mapping regression fixtures and the marked-field benchmark |
models/ |
The standard ball detector and judge, with a README of what each was trained on |
rec5-game/, rec6/, rec7/ |
Pitch maps (pitch/) and ball label sets (labels*/, with their frames) from three more games: an orange ball, the first daylight game, and a night game with a yellow ball, with the cameras the pitch maps were made with (phase0/cameras.json). rec5-game and rec7 also have team sides (teams/), and rec7 a play-box set (playeval/). Their full videos are not included |
rec8/ |
A fourth game outside the bench (night, 2026-11-11): its pitch map, cameras and team sides, and three play-box sets (playeval/seg0-600, seg0-3000, seg0-3600) of 10 plays the follow cam missed. From 54:37 on only Main has footage, so those panoramas show its half of the pitch. Its full video is not included |
A pitch map (pitch/) holds the fitted pitch (pitch.json), the median
background panorama it was fitted on (background.png, 0.03 deg per pixel)
and the hand-tapped keypoints twice: taps.json in that panorama's degrees,
and taps_src.json as pixels in each phone's 4K frame, which don't depend on
the stitch. rec7's pitch map (since 2026-11-09) comes from a joint fit of
both phones' lenses and the pitch, with no warp terms; rec5-game's and
rec6's are the older stitch plus warped pitch fit.
A ball label set holds items.json (the moments and frames picked for
labelling), labels.json (the ball positions, or none where it isn't
visible), images/ (the labelled frames) and pano/ (the stitched
frames). A set with heldout.json is kept for testing: don't train on it.
A play-box set (playeval/<set>/) holds the must-see moments of one clip
that a follow cam should keep in view: clip.json (the clip),
boxes.json (the reviewed box at each moment) and items.json, with the
panoramas they were drawn on (pano/, pano.json).
Team sides (teams/<clip>/sides.json) say which kit components are each
team and which are a keeper or referee. Where automatic side detection
failed they were set by hand (sides.auto.json is what it guessed).
Getting it
After your request is approved, from a checkout of
opensportsball-processing:
hf auth login # a read token is enough
uv run processing/spikes/sharedata.py fetch # everything (~37 GB)
uv run processing/spikes/sharedata.py fetch --annotations-only # pitch maps and labels only (~1 GB)
uv run processing/spikes/benchstore.py # check the bench video
docs/TESTING-PROCESSING.md in that repository describes each benchmark and
how to run it.
Licence
The data is not under an open licence. It is shared only under the terms you accept when requesting access. The code that uses it is AGPL-3.0.
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