HomeIoT ModelsMobileNet-v2-Quantized

MobileNet-v2-Quantized

Imagenet classifier and general purpose backbone.

MobileNetV2 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.

0.30ms
Inference Time
0-2MB
Memory Usage
72NPU
Layers

Technical Details

Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:3.49M
Model size:3.42 MB

Applicable Scenarios

  • Medical Imaging
  • Anomaly Detection
  • Inventory Management

Licenses

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

Tags

  • backbone
    A “backbone” model is designed to extract task-agnostic representations from specific data modalities (e.g., images, text, speech). This representation can then be fine-tuned for specialized tasks.
  • real-time
    A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.
  • 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