--- title: Swin-Tiny canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Tiny" md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Tiny.md" repository: Qualcomm-AI-Hub/Swin-Tiny 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-Tiny > Swin-Tiny - Qualcomm-AI-Hub 在 ModelScope 开源的模型。Swin-Tiny: Optimized for Qualcomm Devices Qualcomm-AI-Hub/Swin-Tiny 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。 - **Repository**: Qualcomm-AI-Hub/Swin-Tiny - **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-Tiny --- ![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/swin_tiny/web-assets/model_demo.png) # Swin-Tiny: Optimized for Qualcomm Devices SwinTiny 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-Tiny 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_tiny) 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_tiny/releases/v0.62.1/swin_tiny-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_tiny/releases/v0.62.1/swin_tiny-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_tiny/releases/v0.62.1/swin_tiny-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_tiny/releases/v0.62.1/swin_tiny-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_tiny/releases/v0.62.1/swin_tiny-tflite-float.zip) For more device-specific assets and performance metrics, visit **[Swin-Tiny on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/swin_tiny)**. ### 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_tiny) 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-Tiny on GitHub](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/swin_tiny) for usage instructions. ## Model Details **Model Type:** Model_use_case.image_classification **Model Stats:** - Input resolution: 224x224 - Model checkpoint: Imagenet - Model size (float): 110 MB - Model size (w8a16): 29.9 MB - Number of parameters: 28.8M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | Swin-Tiny | ONNX | float | Snapdragon® X2 Elite | 4.052 ms | 2 - 2 MB | NPU | Swin-Tiny | ONNX | float | Snapdragon® X Elite | 9.476 ms | 58 - 58 MB | NPU | Swin-Tiny | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 5.92 ms | 1 - 269 MB | NPU | Swin-Tiny | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 13.83 ms | 0 - 255 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 8.914 ms | 0 - 4 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 8.887 ms | 0 - 85 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® QCS8450 | 13.83 ms | 0 - 255 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 9.906 ms | 0 - 4 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 9.476 ms | 58 - 58 MB | NPU | Swin-Tiny | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 4.511 ms | 0 - 443 MB | NPU | Swin-Tiny | ONNX | float | Snapdragon® 8 Elite Mobile | 4.511 ms | 0 - 443 MB | NPU | Swin-Tiny | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.691 ms | 0 - 449 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® X2 Elite | 3.466 ms | 1 - 1 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® X Elite | 8.222 ms | 31 - 31 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 5.135 ms | 0 - 330 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 9.702 ms | 0 - 276 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 24.544 ms | 0 - 3 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 7.081 ms | 0 - 4 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 7.8 ms | 0 - 248 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® QCS8450 | 9.702 ms | 0 - 276 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 8.144 ms | 0 - 3 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 8.222 ms | 31 - 31 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 34.677 ms | 0 - 385 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 8.259 ms | 0 - 235 MB | NPU | Swin-Tiny | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 3.981 ms | 0 - 265 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 3.981 ms | 0 - 265 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.268 ms | 0 - 273 MB | NPU | Swin-Tiny | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 8.259 ms | 0 - 235 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® X2 Elite | 4.468 ms | 1 - 1 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® X Elite | 9.943 ms | 1 - 1 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 5.976 ms | 0 - 246 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 14.524 ms | 0 - 241 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 9.003 ms | 1 - 4 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 9.129 ms | 1 - 208 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® SA8775P | 10.433 ms | 1 - 435 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® SA8650P | 10.433 ms | 1 - 435 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® SA8255P | 10.433 ms | 1 - 435 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® QCS8450 | 14.524 ms | 0 - 241 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 10.265 ms | 3 - 5 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 9.943 ms | 1 - 1 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 4.521 ms | 0 - 414 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® SA7255P | 21.27 ms | 1 - 175 MB | NPU | Swin-Tiny | QNN_DLC | float | Qualcomm® SA8295P | 13.782 ms | 0 - 433 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 4.521 ms | 0 - 414 MB | NPU | Swin-Tiny | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.766 ms | 1 - 414 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 4.414 ms | 0 - 0 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® X Elite | 10.928 ms | 0 - 0 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 6.63 ms | 0 - 510 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 8.827 ms | 0 - 3 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 10.131 ms | 0 - 195 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® SA8775P | 10.637 ms | 0 - 442 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® SA8650P | 10.637 ms | 0 - 442 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® SA8255P | 10.637 ms | 0 - 442 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 10.501 ms | 0 - 2 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 10.928 ms | 0 - 0 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 42.762 ms | 0 - 425 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 10.673 ms | 0 - 435 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 4.895 ms | 0 - 431 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Qualcomm® SA7255P | 17.8 ms | 0 - 437 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 4.895 ms | 0 - 431 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.892 ms | 0 - 437 MB | NPU | Swin-Tiny | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 10.673 ms | 0 - 435 MB | NPU | Swin-Tiny | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 6.067 ms | 0 - 272 MB | NPU | Swin-Tiny | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 14.336 ms | 0 - 254 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 9.055 ms | 0 - 61 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 9.253 ms | 0 - 5 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® SA8775P | 10.438 ms | 0 - 201 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® SA8650P | 10.438 ms | 0 - 201 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® SA8255P | 10.438 ms | 0 - 201 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® QCS8450 | 14.336 ms | 0 - 254 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 10.178 ms | 0 - 60 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 4.604 ms | 0 - 199 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® SA7255P | 21.213 ms | 0 - 199 MB | NPU | Swin-Tiny | TFLITE | float | Qualcomm® SA8295P | 13.895 ms | 0 - 199 MB | NPU | Swin-Tiny | TFLITE | float | Snapdragon® 8 Elite Mobile | 4.604 ms | 0 - 199 MB | NPU | Swin-Tiny | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.836 ms | 0 - 213 MB | NPU ## License * The license for the original implementation of Swin-Tiny 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).