asl-recognition-0004

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.

Example

Specification

Metric

Value

Top-1 accuracy (MS-ASL-100)

0.847

GFlops

6.660

MParams

4.133

Source framework

PyTorch*

Inputs

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

Outputs

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

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: