README.md

---
library_name: pytorch
license: other
tags:
- backbone
- bu_auto
- android
pipeline_tag: image-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/web-assets/model_demo.png)

# Swin-Base: Optimized for Qualcomm Devices

SwinBase 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 Swin-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
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/blob/v0.59.0/src/qai_hub_models/models/swin_base) 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.com) 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://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/releases/v0.59.0/swin_base-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/releases/v0.59.0/swin_base-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/releases/v0.59.0/swin_base-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/releases/v0.59.0/swin_base-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swin_base/releases/v0.59.0/swin_base-tflite-float.zip)

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


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/swin_base) 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 [Swin-Base on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/swin_base) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 88.8M
- Model size (float): 339 MB
- Model size (w8a16): 90.2 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Swin-Base | ONNX | float | Snapdragon® X2 Elite | 8.384 ms | 2 - 2 MB | NPU
| Swin-Base | ONNX | float | Snapdragon® X Elite | 19.605 ms | 176 - 176 MB | NPU
| Swin-Base | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 12.785 ms | 0 - 536 MB | NPU
| Swin-Base | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 29.317 ms | 1 - 525 MB | NPU
| Swin-Base | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.881 ms | 1 - 4 MB | NPU
| Swin-Base | ONNX | float | Qualcomm® QCS8450 | 29.317 ms | 1 - 525 MB | NPU
| Swin-Base | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 22.459 ms | 0 - 4 MB | NPU
| Swin-Base | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 19.605 ms | 176 - 176 MB | NPU
| Swin-Base | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 9.701 ms | 1 - 387 MB | NPU
| Swin-Base | ONNX | float | Snapdragon® 8 Elite Mobile | 9.701 ms | 1 - 387 MB | NPU
| Swin-Base | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.959 ms | 1 - 411 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® X2 Elite | 6.967 ms | 1 - 1 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® X Elite | 16.251 ms | 92 - 92 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 10.427 ms | 0 - 538 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 20.499 ms | 0 - 540 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 57.602 ms | 0 - 3 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.409 ms | 0 - 100 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® QCS8450 | 20.499 ms | 0 - 540 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 16.494 ms | 0 - 3 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 16.251 ms | 92 - 92 MB | NPU
| Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 8.176 ms | 0 - 420 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 8.176 ms | 0 - 420 MB | NPU
| Swin-Base | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 6.531 ms | 0 - 434 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® X2 Elite | 8.398 ms | 1 - 1 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® X Elite | 19.404 ms | 1 - 1 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.423 ms | 0 - 517 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 29.086 ms | 0 - 502 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 53.644 ms | 1 - 363 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.404 ms | 1 - 3 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® SA8775P | 21.38 ms | 1 - 362 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® SA8650P | 21.38 ms | 1 - 362 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® SA8255P | 21.38 ms | 1 - 362 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® QCS8450 | 29.086 ms | 0 - 502 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 19.404 ms | 1 - 1 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 9.418 ms | 1 - 366 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® SA7255P | 53.644 ms | 1 - 363 MB | NPU
| Swin-Base | QNN_DLC | float | Qualcomm® SA8295P | 27.587 ms | 1 - 353 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 9.418 ms | 1 - 366 MB | NPU
| Swin-Base | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.547 ms | 0 - 386 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 8.498 ms | 0 - 0 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® X Elite | 20.539 ms | 0 - 0 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 12.791 ms | 0 - 507 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8275 | 35.425 ms | 0 - 412 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.158 ms | 0 - 359 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8775P | 19.58 ms | 0 - 411 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8650P | 19.58 ms | 0 - 411 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8255P | 19.58 ms | 0 - 411 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 19.809 ms | 0 - 2 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 20.539 ms | 0 - 0 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 123.141 ms | 0 - 929 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 21.594 ms | 0 - 605 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 9.514 ms | 0 - 407 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA7255P | 35.425 ms | 0 - 412 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 9.514 ms | 0 - 407 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 7.619 ms | 0 - 421 MB | NPU
| Swin-Base | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 21.594 ms | 0 - 605 MB | NPU
| Swin-Base | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 12.734 ms | 0 - 1051 MB | NPU
| Swin-Base | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 29.029 ms | 0 - 511 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ QCS8275 | 53.464 ms | 0 - 710 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.779 ms | 0 - 4 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® SA8775P | 21.969 ms | 0 - 376 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® SA8650P | 21.969 ms | 0 - 376 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® SA8255P | 21.969 ms | 0 - 376 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® QCS8450 | 29.029 ms | 0 - 511 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 22.083 ms | 0 - 178 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 9.677 ms | 0 - 379 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® SA7255P | 53.464 ms | 0 - 710 MB | NPU
| Swin-Base | TFLITE | float | Qualcomm® SA8295P | 28.357 ms | 0 - 372 MB | NPU
| Swin-Base | TFLITE | float | Snapdragon® 8 Elite Mobile | 9.677 ms | 0 - 379 MB | NPU
| Swin-Base | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.935 ms | 0 - 398 MB | NPU

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

## References
* [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).