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Videomae Tennis Shottype

Fine-tuned VideoMAE-base (86M params) for 5-class tennis shot classification from 16-frame video clips.

Sourcehuggingface.co/vishnushenoy09/videomae-tennis-shottype/tree/23c812e8fd3d0cf6995694c0462344a0d87e26d9
KindOtherBase modelMCG-NJU/videomae-baseVersionRevision 23c812eBgrade

Model source

Kind
Other
File size
329 MB
Base model
MCG-NJU/videomae-base
Version
vRevision 23c812e
Updated
Oct 24, 2026
Downloads
259
Source
Hugging Face

Source description

VideoMAE Tennis Shot Type Classifier

Fine-tuned VideoMAE-base (86M params) for 5-class tennis shot classification from 16-frame video clips.

Classes

LabelID
Topspin0
Slice1
Volley2
Flat3
Smash4

Training Details

  • 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

Usage

from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification

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

Credits

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.

Sources

1 source
Hugging FacePrimaryVersion 23c812e8fd3d0cf6995694c0462344a0d87e26d9
huggingface.co/vishnushenoy09/videomae-tennis-shottype/tree/23c812e8fd3d0cf6995694c0462344a0d87e26d9

Verified Oct 24

Model artifacts

1 artifact

model.safetensors

safetensors · 329 MB · SHA-256 884ed2d6fb39…8323 · Hugging Face

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Source excerpts

3 excerpts
ReadmeAug 6huggingface.co/vishnushenoy09/videomae-tennis-shottype

--- license: cc-by-4.0 language: - en tags: - video-classification - videomae - tennis - sports - shot-type - fine-tuned base_model: MCG-NJU/videomae-base pipeline_tag: video-classification --- # VideoMAE Tennis Shot Type Classifier Fine-tuned [VideoMAE-base](https://huggingface.co/MCG-NJU/videomae-base) (86M params) for **5-class tennis shot classification** from 16-frame video clips. ## Classes | Label | ID | |-------|-----| | Topspin | 0 | | Slice | 1 | | Volley | 2 | | Flat | 3 | | Smash | 4 | ## Training Details - **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 ## Usage ```python from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification processor = VideoMAEImageProcessor.from_pretrained("vishnushenoy09/videomae-tennis-shottype") model = VideoMAEForVideoClassification.from_pretrained("vishnushenoy09/videomae-tennis-shottype") ``` ## Credits - **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](https://arxiv.org/abs/2203.12602) (NeurIPS 2022). - **TenniSet** dataset by Kacper Wozniak. Used for training data annotation. - **Ball tracking and bounce detection** adapted from [yastrebksv/TrackNet](https://github.com/yastrebksv/TrackNet) and [yastrebksv/TennisProject](https://github.com/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.

NoteOct 24huggingface.co/vishnushenoy09/videomae-tennis-shottype

Source context: 259 downloads · 0 likes · Pipeline video-classification · Repo vishnushenoy09/videomae-tennis-shottype

NoteAug 6huggingface.co/vishnushenoy09/videomae-tennis-shottype

Source context: 16 downloads · 0 likes · Pipeline video-classification · Repo vishnushenoy09/videomae-tennis-shottype