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Shufflenet-v2Quantized

Imagenet classifier and general purpose backbone.

ShufflenetV2 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

Snapdragon® X Elite
TorchScripttoQualcomm® AI Engine Direct
0.69ms
Inference Time
1MB
Memory Usage
122NPU
Layers

Technical Details

Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:1.37M
Model size:4.42 MB

Applicable Scenarios

  • Medical Imaging
  • Anomaly Detection
  • Inventory Management

Licenses

Source Model:BSD-3-CLAUSE
Deployable Model:AI Model Hub License

Tags

  • quantized
    A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

Supported Compute Chipsets

  • Snapdragon® X Elite