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ResNeXt101Quantized

Imagenet classifier and general purpose backbone.

ResNeXt101 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:88.7M
Model size:87.3 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.
  • quantized
    A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

Supported Compute Chipsets

  • Snapdragon® X Elite