volleyball-person-ball-detector

Volleyball person + ball detector (D-FINE-S fine-tune)

A two-class real-time object detector for volleyball footage: person and ball. It is a fine-tune of D-FINE-S (10 M parameters, Apache-2.0) trained on frames from youth club matches filmed with a wide-angle camera mounted on the referee stand, plus beach and grass sessions from the same camera rig.

The model was built to power automatic highlight detection in a volleyball recording app: it finds every person in the frame and the ball, and the app decides who is a player from the court region.

What it detects

It does not distinguish players from spectators. Filter by court region downstream if you need players only.

Intended use and tiling

The ball is small: in 4K footage from a ref-stand camera it is often 20 to 40 pixels across. The model was trained on 640×640 tiles cut from 720 to 1040 px windows of 4K frames, so it should be run the same way:

• For 1080p or smaller images, run on the whole image.
• For 4K frames, slice into ~860 px tiles with 25 % overlap, run each tile, add one full-frame pass, then merge with NMS.

predict.py in this repo does exactly that.

from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import torch

repo = "grdesignbuild/volleyball-person-ball-detector"
processor = AutoImageProcessor.from_pretrained(repo)
model = AutoModelForObjectDetection.from_pretrained(repo).eval()

img = Image.open("frame.jpg").convert("RGB")           # a tile or a small image
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
    out = model(**inputs)
res = processor.post_process_object_detection(out, threshold=0.4, target_sizes=[img.size[::-1]])[0]
for score, label, box in zip(res["scores"], res["labels"], res["boxes"]):
    print(model.config.id2label[label.item()], round(score.item(), 2), [round(v) for v in box.tolist()])

Training data

• Source: 37 recordings (about 880 minutes) from one club season: indoor multi-court convention halls (AAU-style tournaments), a local gym, beach courts and grass courts. 4K at 30 or 60 fps, one 1080p sideline video.
• Frames: 3,061 keyframes sampled evenly across each recording, skipping warm-ups — 2,940 from the ref-stand rig plus, new in this release, 121 from an end-line camera (behind the baseline, 2000×1124 H.264) so the model sees players close and large and the ball from behind the court.
• Labels: pseudo-labels from D-FINE-X Objects365 run on overlapping tiles of each frame (classes Person and Volleyball), with every accepted ball box on the ref-stand frames reviewed by hand (4,077 of 6,086: 910 removed as ceiling lights, coolers, cups, bags and logos; 456 blurred out as ignore regions) and every accepted ball box on the end-line frames reviewed by hand (136: 77 were three ceiling-light fixtures repeated across frames, 44 real balls kept, 15 far-court specks blurred out). Person boxes are the teacher's, unreviewed. Tiles with people but no ball are sampled at 55% so shoes, knee pads and hair are seen as background often; end-line tiles are repeated three times so they are not drowned by the ref-stand ones.
• Split by recording: 31,023 training records (30,043 tiles, end-line ones ×3), 4,181 validation tiles from 5 held-out ref-stand recordings covering each venue type.
• The training images contain minors and are not published.

Evaluation

Validation is against the teacher's pseudo-labels on held-out ref-stand recordings, with the ball-label cleaning applied to the validation side. These numbers are comparable to the 2026-09-08 release (same validation labels), not to the first release (0.759 [email protected] against uncleaned labels).

Training: fine-tuned from the 2026-09-08 release for 1 epoch, batch 8, AdamW (lr 5e-5, backbone 5e-6), cosine schedule, on an Apple M5 GPU.

Held-out match footage, ref-stand camera (24 frames, 4K, no release trained on them)

End-line camera: nine hand-labelled contact frames, 640 px tiles

Every real ball scores higher than under the previous release; the ceiling-light false positive on that venue's pillar disappears above 0.47. Run this model with tiles of ~640 px on frames around 2000 px wide, not the 4K recipe above.

Limitations

• Trained on one camera rig and one age group; expect weaker results on broadcast TV angles.
• Balls on adjacent courts and balls in ball carts are detected too. That is correct behaviour, but you may need to filter.
• Fast-moving, heavily motion-blurred balls are the most common miss, and since the 2026-09-08 release the model is a little more conservative at the exact instant a blurred ball meets a player's hands — the trade for the false-positive reduction above.
• Shadows and mannequin-like objects occasionally score as person at low confidence; a 0.4 threshold is a good default.

Version history

• 2026-09-08, end-line release — 121 end-line frames added (ball labels hand-cleaned), end-line tiles oversampled ×3, 1-epoch fine-tune from the 2026-09-08 release. Finds the ball from an end-line camera where the previous release mostly found a ceiling light; ref-stand behaviour unchanged.
• 2026-09-08 — ball labels hand-cleaned, background sampling raised, 1-epoch fine-tune from the first release. 91% fewer false ball claims on held-out match footage; person detection unchanged.
• 2026-09-03 — first release, teacher pseudo-labels only.

License

Apache-2.0, same as the base model. Built with 🤗 Transformers.