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SqueezeNet-1_1Quantized

Imagenet classifier and general purpose backbone.

SqueezeNet 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.

Model RepositoryHugging FaceResearch Paper

Technical Details

Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:1.24M
Model size:1.30 MB

Applicable Scenarios

  • Gaming
  • Robotics

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.
  • 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