Use Case and High-Level Description

A human gesture recognition model for the American Sign Language (ASL) recognition scenario (word-level recognition). The model uses an S3D framework with MobileNet V3 backbone. Please refer to the MS-ASL-100* dataset specification to see the list of gestures that are recognized by this model.

The model accepts a stack of frames sampled with a constant framerate (15 FPS) and produces a prediction on the input clip.



Metric Value
Top-1 accuracy (MS-ASL-100*) 0.847
GFlops 6.660
MParams 4.133
Source framework PyTorch*



Name: input, shape: [1x3x16x224x224]. An input image sequence in the format [BxCxTxHxW], where:

  • B - batch size
  • C - number of channels
  • T - duration of input clip
  • H - image height
  • W - image width


The model outputs a tensor with the shape [Bx100], each row is a logits vector of performed ASL gestures.

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