--- title: Swin-Small canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Small" md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/Swin-Small.md" repository: Qualcomm-AI-Hub/Swin-Small 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-Small > Swin-Small - Qualcomm-AI-Hub 在 ModelScope 开源的模型。Swin-Small: Optimized for Qualcomm Devices Qualcomm-AI-Hub/Swin-Small 是 ModelScope 魔搭社区上的image-classification模型，采用 bsd-3-clause 许可。 - **Repository**: Qualcomm-AI-Hub/Swin-Small - **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-Small --- ![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/swin_small/web-assets/model_demo.png) # Swin-Small: Optimized for Qualcomm Devices SwinSmall 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-Small 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_small) 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_small/releases/v0.62.1/swin_small-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_small/releases/v0.62.1/swin_small-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_small/releases/v0.62.1/swin_small-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_small/releases/v0.62.1/swin_small-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_small/releases/v0.62.1/swin_small-tflite-float.zip) For more device-specific assets and performance metrics, visit **[Swin-Small on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/swin_small)**. ### 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_small) 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-Small on GitHub](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/swin_small) for usage instructions. ## Model Details **Model Type:** Model_use_case.image_classification **Model Stats:** - Input resolution: 224x224 - Model checkpoint: Imagenet - Model size (float): 193 MB - Model size (w8a16): 52.5 MB - Number of parameters: 50.4M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | Swin-Small | ONNX | float | Snapdragon® X2 Elite | 6.877 ms | 2 - 2 MB | NPU | Swin-Small | ONNX | float | Snapdragon® X Elite | 16.218 ms | 101 - 101 MB | NPU | Swin-Small | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 10.147 ms | 0 - 444 MB | NPU | Swin-Small | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 23.418 ms | 1 - 432 MB | NPU | Swin-Small | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 15.546 ms | 0 - 4 MB | NPU | Swin-Small | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.379 ms | 0 - 114 MB | NPU | Swin-Small | ONNX | float | Qualcomm® QCS8450 | 23.418 ms | 1 - 432 MB | NPU | Swin-Small | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 17.716 ms | 1 - 4 MB | NPU | Swin-Small | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 16.218 ms | 101 - 101 MB | NPU | Swin-Small | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 7.811 ms | 1 - 610 MB | NPU | Swin-Small | ONNX | float | Snapdragon® 8 Elite Mobile | 7.811 ms | 1 - 610 MB | NPU | Swin-Small | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.427 ms | 1 - 622 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® X2 Elite | 5.985 ms | 1 - 1 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® X Elite | 13.893 ms | 54 - 54 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 8.726 ms | 0 - 526 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 16.526 ms | 0 - 437 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 40.392 ms | 0 - 3 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 11.846 ms | 0 - 4 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 13.291 ms | 0 - 58 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® QCS8450 | 16.526 ms | 0 - 437 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 13.995 ms | 0 - 3 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 13.893 ms | 54 - 54 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 56.588 ms | 2 - 557 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 13.964 ms | 0 - 388 MB | NPU | Swin-Small | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 6.859 ms | 0 - 439 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® 8 Elite Mobile | 6.859 ms | 0 - 439 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 5.643 ms | 0 - 449 MB | NPU | Swin-Small | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 13.964 ms | 0 - 388 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® X2 Elite | 7.129 ms | 1 - 1 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® X Elite | 16.331 ms | 1 - 1 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 9.928 ms | 1 - 407 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 23.881 ms | 0 - 395 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 15.602 ms | 1 - 4 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.337 ms | 1 - 4 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® SA8775P | 17.421 ms | 1 - 593 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® SA8650P | 17.421 ms | 1 - 593 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® SA8255P | 17.421 ms | 1 - 593 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® QCS8450 | 23.881 ms | 0 - 395 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 17.63 ms | 3 - 5 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 16.331 ms | 1 - 1 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 7.594 ms | 1 - 587 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® SA7255P | 37.94 ms | 1 - 275 MB | NPU | Swin-Small | QNN_DLC | float | Qualcomm® SA8295P | 22.646 ms | 1 - 270 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 7.594 ms | 1 - 587 MB | NPU | Swin-Small | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.247 ms | 0 - 600 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 7.251 ms | 0 - 0 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® X Elite | 17.485 ms | 0 - 0 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 10.777 ms | 0 - 870 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 14.372 ms | 0 - 3 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 16.207 ms | 0 - 3 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® SA8775P | 16.763 ms | 0 - 638 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® SA8650P | 16.763 ms | 0 - 638 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® SA8255P | 16.763 ms | 0 - 638 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 17.054 ms | 2 - 4 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 17.485 ms | 0 - 0 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 73.029 ms | 0 - 751 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 17.377 ms | 0 - 652 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 8.085 ms | 0 - 623 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Qualcomm® SA7255P | 29.247 ms | 0 - 636 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® 8 Elite Mobile | 8.085 ms | 0 - 623 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 6.501 ms | 0 - 640 MB | NPU | Swin-Small | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 17.377 ms | 0 - 652 MB | NPU | Swin-Small | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 10.181 ms | 0 - 434 MB | NPU | Swin-Small | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 23.425 ms | 0 - 411 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 15.575 ms | 0 - 104 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.589 ms | 0 - 4 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® SA8775P | 17.46 ms | 0 - 306 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® SA8650P | 17.46 ms | 0 - 306 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® SA8255P | 17.46 ms | 0 - 306 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® QCS8450 | 23.425 ms | 0 - 411 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 18.096 ms | 0 - 104 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 7.797 ms | 0 - 296 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® SA7255P | 37.377 ms | 0 - 303 MB | NPU | Swin-Small | TFLITE | float | Qualcomm® SA8295P | 22.992 ms | 0 - 297 MB | NPU | Swin-Small | TFLITE | float | Snapdragon® 8 Elite Mobile | 7.797 ms | 0 - 296 MB | NPU | Swin-Small | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.599 ms | 0 - 320 MB | NPU ## License * The license for the original implementation of Swin-Small 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).