Beach Volley Vision β Fine-tuned TrackNetV3 (Ball Tracking)
A fine-tuned TrackNetV3 model for tracking the ball in beach volleyball footage. TrackNetV3 is a heatmap-based tracker designed for small, fast-moving sports objects (originally shuttlecocks); this checkpoint adapts it to the beach volleyball domain.
This is a derivative of the upstream TrackNetV3 model, fine-tuned on hand-annotated beach volleyball rally clips. The upstream project is MIT licensed, and this derivative carries the same license forward (see License).
Model details
- Architecture: TrackNetV3 (trajectory-prediction module). This repo contains the
fine-tuned
TrackNetweights only. - Base model:
qaz812345/TrackNetV3 - Task: per-frame ball localization (heatmap β (x, y) + visibility)
- Domain: beach volleyball match video
- File:
tracknet_best.pt(~130 MB)
Note on InpaintNet: the trajectory-rectification (inpainting) module was not retrained. If your pipeline uses it, download
InpaintNet_best.ptfrom the upstream TrackNetV3 repo.
Usage
This checkpoint is consumed by the Beach Volley Vision pipeline: π https://github.com/ddecks/beach-volley-vision
# Download the fine-tuned weights (git-lfs required)
git lfs install
git clone git@hf.co:deadfast/beach-volley-vision-models
# or grab the single file:
# https://huggingface.co/deadfast/beach-volley-vision-models/resolve/main/tracknet_best.pt
# Place it where the pipeline expects it
mkdir -p data/models
cp beach-volley-vision-models/tracknet_best.pt data/models/tracknet_best.pt
See the project repository for the full inference pipeline (detection β tracking β rally/event detection β stats).
Training data
Fine-tuned on hand-annotated beach volleyball rally clips β frames labeled with ball
position and visibility, exported to the TrackNet CSV format
(Frame,Visibility,X,Y). Example annotation and evaluation data is included in the
project repository under data/.
License
MIT β carried forward from the upstream TrackNetV3 project (Copyright (c) 2024 qaz812345). Fine-tuning and beach volleyball adaptation by Devin Decker.
Acknowledgements
- TrackNetV3 by qaz812345 β the base architecture and pretrained weights this model is fine-tuned from.