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 sizeC
- number of channelsT
- duration of input clipH
- image heightW
- image width
Outputs#
The model outputs a tensor with the shape 1, 100
in the format B, L
, where:
B
- batch sizeL
- 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:
Legal Information#
[*] Other names and brands may be claimed as the property of others.