---
title: Beit
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Beit"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Beit.md"
repository: Qualcomm-AI-Hub/Beit
last_updated: 2026-10-16
license: bsd-3-clause
pipeline_tag: image-classification
tasks:
  - image-classification
downloads: 0
stars: 0
tags:
  - backbone
  - android
---

# Beit

> Beit - Qualcomm-AI-Hub 在 ModelScope 开源的模型。Beit: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/Beit 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。

- **Repository**: Qualcomm-AI-Hub/Beit
- **License**: bsd-3-clause
- **Tasks**: image-classification
- **Tags**: backbone, android
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/Beit

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/web-assets/model_demo.png)

# 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](https://github.com/microsoft/unilm/tree/master/beit).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/beit) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.cn) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) 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 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/releases/v0.62.1/beit-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/releases/v0.62.1/beit-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/releases/v0.62.1/beit-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/releases/v0.62.1/beit-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/beit/releases/v0.62.1/beit-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[Beit on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/beit)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/beit) 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](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/beit) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

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

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Beit | ONNX | float | Snapdragon® X2 Elite | 7.44 ms | 2 - 2 MB | NPU
| Beit | ONNX | float | Snapdragon® X Elite | 15.399 ms | 184 - 184 MB | NPU
| Beit | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 10.531 ms | 0 - 449 MB | NPU
| Beit | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 17.916 ms | 1 - 422 MB | NPU
| Beit | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 17.891 ms | 0 - 4 MB | NPU
| Beit | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 14.87 ms | 0 - 194 MB | NPU
| Beit | ONNX | float | Qualcomm® QCS8450 | 17.916 ms | 1 - 422 MB | NPU
| Beit | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 17.923 ms | 0 - 4 MB | NPU
| Beit | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 15.399 ms | 184 - 184 MB | NPU
| Beit | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 8.548 ms | 0 - 305 MB | NPU
| Beit | ONNX | float | Snapdragon® 8 Elite Mobile | 8.548 ms | 0 - 305 MB | NPU
| Beit | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.168 ms | 0 - 305 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® X2 Elite | 2.667 ms | 1 - 1 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® X Elite | 6.89 ms | 95 - 95 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 4.579 ms | 0 - 421 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 11.217 ms | 0 - 424 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 36.124 ms | 0 - 3 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 6.266 ms | 0 - 4 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 6.703 ms | 0 - 3 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® QCS8450 | 11.217 ms | 0 - 424 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 6.637 ms | 0 - 3 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 6.89 ms | 95 - 95 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 64.048 ms | 1 - 441 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 9.022 ms | 0 - 409 MB | NPU
| Beit | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 3.595 ms | 0 - 361 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 3.595 ms | 0 - 361 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.398 ms | 0 - 256 MB | NPU
| Beit | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 9.022 ms | 0 - 409 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® X2 Elite | 7.859 ms | 1 - 1 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® X Elite | 15.153 ms | 1 - 1 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 10.476 ms | 0 - 392 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 17.563 ms | 0 - 382 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 17.676 ms | 1 - 4 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 14.357 ms | 1 - 2 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® SA8775P | 17.181 ms | 1 - 296 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® SA8650P | 17.181 ms | 1 - 296 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® SA8255P | 17.181 ms | 1 - 296 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® QCS8450 | 17.563 ms | 0 - 382 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 17.467 ms | 1 - 3 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 15.153 ms | 1 - 1 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 8.492 ms | 1 - 299 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® SA7255P | 47.953 ms | 1 - 295 MB | NPU
| Beit | QNN_DLC | float | Qualcomm® SA8295P | 15.316 ms | 1 - 289 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 8.492 ms | 1 - 299 MB | NPU
| Beit | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.191 ms | 1 - 302 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 3.194 ms | 0 - 0 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® X Elite | 7.344 ms | 0 - 0 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 4.673 ms | 0 - 411 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 6.288 ms | 0 - 3 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 6.795 ms | 0 - 2 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® SA8775P | 7.044 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® SA8650P | 7.044 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® SA8255P | 7.044 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 6.871 ms | 0 - 2 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 7.344 ms | 0 - 0 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 64.704 ms | 0 - 429 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 8.61 ms | 0 - 399 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 3.559 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Qualcomm® SA7255P | 15.087 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 3.559 ms | 0 - 351 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 2.558 ms | 0 - 247 MB | NPU
| Beit | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 8.61 ms | 0 - 399 MB | NPU
| Beit | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 10.526 ms | 0 - 406 MB | NPU
| Beit | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 17.437 ms | 0 - 372 MB | NPU
| Beit | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 17.925 ms | 0 - 187 MB | NPU
| Beit | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 14.034 ms | 0 - 2 MB | NPU
| Beit | TFLITE | float | Qualcomm® SA8775P | 17.176 ms | 0 - 301 MB | NPU
| Beit | TFLITE | float | Qualcomm® SA8650P | 17.176 ms | 0 - 301 MB | NPU
| Beit | TFLITE | float | Qualcomm® SA8255P | 17.176 ms | 0 - 301 MB | NPU
| Beit | TFLITE | float | Qualcomm® QCS8450 | 17.437 ms | 0 - 372 MB | NPU
| Beit | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 17.393 ms | 0 - 186 MB | NPU
| Beit | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 8.512 ms | 0 - 310 MB | NPU
| Beit | TFLITE | float | Qualcomm® SA7255P | 47.986 ms | 0 - 301 MB | NPU
| Beit | TFLITE | float | Qualcomm® SA8295P | 15.342 ms | 0 - 293 MB | NPU
| Beit | TFLITE | float | Snapdragon® 8 Elite Mobile | 8.512 ms | 0 - 310 MB | NPU
| Beit | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.182 ms | 0 - 313 MB | NPU

## License
* The license for the original implementation of Beit can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
* [Source Model Implementation](https://github.com/microsoft/unilm/tree/master/beit)

## Community
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).