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Beit

Image Classification
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backbone
android
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metadata 
library_name: pytorch
license: other
tags:
  - backbone
  - android
pipeline_tag: image-classification

Beit: Optimized for Qualcomm Devices

Beit 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.

This is based on the implementation of Beit found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

Runtime Precision Chipset SDK Versions Download
ONNX float Universal QAIRT 2.37, ONNX Runtime 1.23.0 Download
QNN_DLC float Universal QAIRT 2.42 Download
QNN_DLC w8a16 Universal QAIRT 2.42 Download
TFLITE float Universal QAIRT 2.42, TFLite 2.17.0 Download

For more device-specific assets and performance metrics, visit Beit on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

  • Custom weights (e.g., fine-tuned checkpoints)
  • Custom input shapes
  • Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for Beit on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

  • Model checkpoint: Imagenet
  • Input resolution: 224x224
  • Number of parameters: 92.0M
  • Model size (float): 351 MB

Performance Summary

Model Runtime Precision Chipset Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit
Beit ONNX float Snapdragon® X Elite 14.768 ms 186 - 186 MB NPU
Beit ONNX float Snapdragon® 8 Gen 3 Mobile 9.876 ms 0 - 524 MB NPU
Beit ONNX float Qualcomm® QCS8550 (Proxy) 13.457 ms 0 - 194 MB NPU
Beit ONNX float Qualcomm® QCS9075 20.562 ms 0 - 4 MB NPU
Beit ONNX float Snapdragon® 8 Elite For Galaxy Mobile 7.167 ms 1 - 447 MB NPU
Beit ONNX float Snapdragon® 8 Elite Gen 5 Mobile 5.932 ms 0 - 436 MB NPU
Beit QNN_DLC float Snapdragon® X Elite 13.534 ms 1 - 1 MB NPU
Beit QNN_DLC float Snapdragon® 8 Gen 3 Mobile 8.535 ms 0 - 535 MB NPU
Beit QNN_DLC float Qualcomm® QCS8275 (Proxy) 44.873 ms 1 - 485 MB NPU
Beit QNN_DLC float Qualcomm® QCS8550 (Proxy) 12.732 ms 1 - 2 MB NPU
Beit QNN_DLC float Qualcomm® SA8775P 15.563 ms 1 - 485 MB NPU
Beit QNN_DLC float Qualcomm® QCS9075 16.84 ms 1 - 3 MB NPU
Beit QNN_DLC float Qualcomm® QCS8450 (Proxy) 22.993 ms 0 - 507 MB NPU
Beit QNN_DLC float Qualcomm® SA7255P 44.873 ms 1 - 485 MB NPU
Beit QNN_DLC float Qualcomm® SA8295P 19.001 ms 1 - 468 MB NPU
Beit QNN_DLC float Snapdragon® 8 Elite For Galaxy Mobile 7.003 ms 1 - 478 MB NPU
Beit QNN_DLC float Snapdragon® 8 Elite Gen 5 Mobile 6.475 ms 1 - 481 MB NPU
Beit TFLITE float Snapdragon® 8 Gen 3 Mobile 6.665 ms 0 - 350 MB NPU
Beit TFLITE float Qualcomm® QCS8275 (Proxy) 38.644 ms 0 - 302 MB NPU
Beit TFLITE float Qualcomm® QCS8550 (Proxy) 9.671 ms 0 - 3 MB NPU
Beit TFLITE float Qualcomm® SA8775P 12.131 ms 0 - 310 MB NPU
Beit TFLITE float Qualcomm® QCS9075 13.331 ms 0 - 187 MB NPU
Beit TFLITE float Qualcomm® QCS8450 (Proxy) 19.271 ms 0 - 433 MB NPU
Beit TFLITE float Qualcomm® SA7255P 38.644 ms 0 - 302 MB NPU
Beit TFLITE float Qualcomm® SA8295P 16.047 ms 0 - 410 MB NPU
Beit TFLITE float Snapdragon® 8 Elite For Galaxy Mobile 4.824 ms 0 - 302 MB NPU
Beit TFLITE float Snapdragon® 8 Elite Gen 5 Mobile 4.065 ms 0 - 304 MB NPU

License

  • The license for the original implementation of Beit can be found here.

References

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