from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification

processor = VideoMAEImageProcessor.from_pretrained("vishnushenoy09/videomae-tennis-shottype")
model = VideoMAEForVideoClassification.from_pretrained("vishnushenoy09/videomae-tennis-shottype")

Credits

Dataset: 2,436 annotated hit events from TenniSet (videos V006-V010)
Input: 16 frames x 224x224, sampled around each hit timestamp
Accuracy: 88-90% on held-out test split
Hardware: Apple M4 Pro (48GB, MPS)
Framework: HuggingFace Transformers

VideoMAE by Zhan Tong, Yibing Song, Jue Wang, Limin Wang (MCG-NJU). Paper: VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training (NeurIPS 2022).
TenniSet dataset by Kacper Wozniak. Used for training data annotation.
Ball tracking and bounce detection adapted from yastrebksv/TrackNet and yastrebksv/TennisProject.

Part of

This model is part of the Sports Commentator from Video project — an automated tennis commentary pipeline using TrackNet, CatBoost, VideoMAE, Qwen2.5, RF-DETR, and F5-TTS.