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videobadminton's Introduction

VideoBadminton

Qi Li ¹, Tzu-Chen Chiu ², Hsiang-Wei Huang ², Min-Te Sun², Wei-Shinn Ku ¹

¹ Auburn University Auburn, United States, ² National Central University Taoyuan City, Taiwan

Dataset

Our dataset is available at VideoBadminton.

ModelZoo

Methods	Backbone	Top1 Acc	Top5 Acc	Mean Cls Acc	Models trained on VideoBadminton
R(2+1)D	ResNet2Plus1d	79.53%	96.11%	66.97%	R(2+1)D
SlowFast	ResNet3dSlowFast	82.80%	97.54%	73.80%	SlowFast
TimeSformer	TimeSformer	73.18%	94.78%	57.70%	TimeSformer
Swim	SwinTransformer3D	81.99%	96.52%	69.93%	Swim
MViT-V2	MViT	14.23%	62.23%	10.76%	MViT-V2
ST-GCN	RecognizerGCN	74.41%	93.76%	61.44%	ST-GCN
PoseC3D	Recognizer3D	80.76%	96.01%	67.18%	PoseC3D

Notes:

The train and validation sets are split as follows:

Citation

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videobadminton's Issues

Requesting checkpoints of the models in the ModelZoo for experimentation

Can you please provide the checkpoints of the models that have been listed in the ModelZoo?

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