--- title: Swin-Base canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Base" md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Base.md" repository: Qualcomm-AI-Hub/Swin-Base last_updated: 2026-10-16 license: bsd-3-clause pipeline_tag: image-classification tasks: - image-classification downloads: 0 stars: 0 tags: - backbone - bu_auto - android --- # Swin-Base > Swin-Base - Qualcomm-AI-Hub 在 ModelScope 开源的模型。Swin-Base: Optimized for Qualcomm Devices Qualcomm-AI-Hub/Swin-Base 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。 - **Repository**: Qualcomm-AI-Hub/Swin-Base - **License**: bsd-3-clause - **Tasks**: image-classification - **Tags**: backbone, bu_auto, android - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Base --- ![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/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-cn/blob/main/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.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/swin_base/releases/v0.62.1/swin_base-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/swin_base/releases/v0.62.1/swin_base-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/swin_base/releases/v0.62.1/swin_base-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/swin_base/releases/v0.62.1/swin_base-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/swin_base/releases/v0.62.1/swin_base-tflite-float.zip) For more device-specific assets and performance metrics, visit **[Swin-Base on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/swin_base)**. ### 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/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-cn/blob/main/src/qai_hub_models/models/swin_base) for usage instructions. ## Model Details **Model Type:** Model_use_case.image_classification **Model Stats:** - Input resolution: 224x224 - Model checkpoint: Imagenet - Model size (float): 339 MB - Model size (w8a16): 90.2 MB - Number of parameters: 88.8M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | Swin-Base | ONNX | float | Snapdragon® X2 Elite | 15.158 ms | 2 - 2 MB | NPU | Swin-Base | ONNX | float | Snapdragon® X Elite | 20.012 ms | 175 - 175 MB | NPU | Swin-Base | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 13.247 ms | 0 - 548 MB | NPU | Swin-Base | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 29.207 ms | 1 - 1065 MB | NPU | Swin-Base | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 21.316 ms | 0 - 4 MB | NPU | Swin-Base | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.223 ms | 0 - 194 MB | NPU | Swin-Base | ONNX | float | Qualcomm® QCS8450 | 29.207 ms | 1 - 1065 MB | NPU | Swin-Base | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 22.522 ms | 1 - 4 MB | NPU | Swin-Base | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 20.012 ms | 175 - 175 MB | NPU | Swin-Base | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 9.865 ms | 0 - 400 MB | NPU | Swin-Base | ONNX | float | Snapdragon® 8 Elite Mobile | 9.865 ms | 0 - 400 MB | NPU | Swin-Base | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.977 ms | 0 - 420 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® X2 Elite | 6.983 ms | 1 - 1 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® X Elite | 16.232 ms | 92 - 92 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 10.52 ms | 0 - 535 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 20.712 ms | 1 - 539 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 58.229 ms | 0 - 3 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 14.217 ms | 0 - 4 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.45 ms | 0 - 99 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® QCS8450 | 20.712 ms | 1 - 539 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 16.302 ms | 0 - 3 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 16.232 ms | 92 - 92 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 98.428 ms | 0 - 679 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 18.059 ms | 0 - 625 MB | NPU | Swin-Base | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 8.213 ms | 0 - 417 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 8.213 ms | 0 - 417 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 6.538 ms | 0 - 429 MB | NPU | Swin-Base | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 18.059 ms | 0 - 625 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® X2 Elite | 8.451 ms | 1 - 1 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® X Elite | 19.327 ms | 1 - 1 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.488 ms | 0 - 516 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 29.023 ms | 1 - 502 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 20.55 ms | 1 - 4 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 18.375 ms | 1 - 3 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® SA8775P | 21.367 ms | 1 - 362 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® SA8650P | 21.367 ms | 1 - 362 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® SA8255P | 21.367 ms | 1 - 362 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® QCS8450 | 29.023 ms | 1 - 502 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 21.542 ms | 1 - 3 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 19.327 ms | 1 - 1 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 9.337 ms | 1 - 363 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® SA7255P | 53.601 ms | 1 - 363 MB | NPU | Swin-Base | QNN_DLC | float | Qualcomm® SA8295P | 27.618 ms | 1 - 353 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 9.337 ms | 1 - 363 MB | NPU | Swin-Base | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.538 ms | 0 - 383 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 8.522 ms | 0 - 0 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® X Elite | 20.587 ms | 0 - 0 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 12.819 ms | 0 - 510 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 17.252 ms | 0 - 3 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.196 ms | 0 - 3 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8775P | 19.574 ms | 0 - 411 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8650P | 19.574 ms | 0 - 411 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA8255P | 19.574 ms | 0 - 411 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 19.933 ms | 0 - 2 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 20.587 ms | 0 - 0 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 123.964 ms | 0 - 929 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 21.699 ms | 0 - 604 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 9.508 ms | 0 - 406 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Qualcomm® SA7255P | 35.433 ms | 0 - 412 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 9.508 ms | 0 - 406 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 7.588 ms | 0 - 421 MB | NPU | Swin-Base | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 21.699 ms | 0 - 604 MB | NPU | Swin-Base | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 12.828 ms | 0 - 1058 MB | NPU | Swin-Base | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 29.133 ms | 0 - 506 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 20.726 ms | 0 - 179 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.13 ms | 0 - 4 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® SA8775P | 21.905 ms | 0 - 375 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® SA8650P | 21.905 ms | 0 - 375 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® SA8255P | 21.905 ms | 0 - 375 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® QCS8450 | 29.133 ms | 0 - 506 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 21.95 ms | 0 - 178 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 9.671 ms | 0 - 386 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® SA7255P | 53.445 ms | 0 - 710 MB | NPU | Swin-Base | TFLITE | float | Qualcomm® SA8295P | 28.296 ms | 0 - 371 MB | NPU | Swin-Base | TFLITE | float | Snapdragon® 8 Elite Mobile | 9.671 ms | 0 - 386 MB | NPU | Swin-Base | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.944 ms | 0 - 396 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 * For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).