HomeIoT ModelsShufflenet-v2Quantized

    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.

    0.67ms
    Inference Time
    0-2MB
    Memory Usage
    207NPU
    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 IoT Devices

    • QCS6490 (Proxy)
    • QCS8250 (Proxy)
    • QCS8550 (Proxy)
    • RB3 Gen 2 (Proxy)
    • RB5 (Proxy)

    Supported IoT Chipsets

    • Qualcomm® QCS6490
    • Qualcomm® QCS8250
    • Qualcomm® QCS8550