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 frame rate (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*


Image sequence, name: input, shape: 1, 3, 16, 224, 224 in the format B, C, T, H, W, 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 1, 100 in the format B, L, where:

  • B - batch size
  • L - logits vector for each performed ASL gestures

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