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Swin-Small

Image Classification
PyTorch
TF Lite
ONNX
backbone
android
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  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.
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- This model is an implementation of Swin-Small found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
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  This repository provides scripts to run Swin-Small on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/swin_small).
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  - Number of parameters: 50.4M
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  - Model size: 193 MB
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- | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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- | ---|---|---|---|---|---|---|---|
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 21.002 ms | 0 - 4 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 23.699 ms | 0 - 37 MB | FP16 | NPU | [Swin-Small.so](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.so)
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-
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.swin_small.export
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  ```
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-
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  ```
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- Profile Job summary of Swin-Small
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 22.65 ms
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- Estimated Peak Memory Range: 0.57-0.57 MB
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- Compute Units: NPU (1246) | Total (1246)
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-
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  ```
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  Get more details on Swin-Small's performance across various devices [here](https://aihub.qualcomm.com/models/swin_small).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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  ## License
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- - The license for the original implementation of Swin-Small can be found
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- [here](https://github.com/pytorch/vision/blob/main/LICENSE).
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- - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
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  ## References
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  * [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:[email protected]).
 
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  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.
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+ This model is an implementation of Swin-Small found [here]({source_repo}).
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  This repository provides scripts to run Swin-Small on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/swin_small).
 
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  - Number of parameters: 50.4M
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  - Model size: 193 MB
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+ | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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+ |---|---|---|---|---|---|---|---|---|
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+ | Swin-Small | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 18.699 ms | 0 - 5 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 21.583 ms | 0 - 38 MB | FP16 | NPU | [Swin-Small.so](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.so) |
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+ | Swin-Small | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 34.575 ms | 0 - 130 MB | FP16 | NPU | [Swin-Small.onnx](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx) |
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+ | Swin-Small | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 12.959 ms | 0 - 526 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 14.588 ms | 0 - 156 MB | FP16 | NPU | [Swin-Small.so](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.so) |
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+ | Swin-Small | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 23.854 ms | 0 - 783 MB | FP16 | NPU | [Swin-Small.onnx](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx) |
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+ | Swin-Small | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 18.642 ms | 0 - 3 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 20.236 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 18.785 ms | 0 - 3 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | SA8255 (Proxy) | SA8255P Proxy | QNN | 20.621 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 18.67 ms | 0 - 5 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | SA8775 (Proxy) | SA8775P Proxy | QNN | 20.685 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 18.664 ms | 0 - 3 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | SA8650 (Proxy) | SA8650P Proxy | QNN | 20.596 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 24.22 ms | 0 - 510 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 26.468 ms | 1 - 155 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 11.752 ms | 2 - 232 MB | FP16 | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
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+ | Swin-Small | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 12.483 ms | 1 - 162 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 20.333 ms | 0 - 313 MB | FP16 | NPU | [Swin-Small.onnx](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx) |
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+ | Swin-Small | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 21.141 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | Swin-Small | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 37.889 ms | 118 - 118 MB | FP16 | NPU | [Swin-Small.onnx](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.swin_small.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ Swin-Small
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 18.7
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+ Estimated peak memory usage (MB): [0, 5]
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+ Total # Ops : 1563
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+ Compute Unit(s) : NPU (1563 ops)
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  ```
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  Get more details on Swin-Small's performance across various devices [here](https://aihub.qualcomm.com/models/swin_small).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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+
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  ## License
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+ * The license for the original implementation of Swin-Small can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE).
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+ * The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
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+
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  ## References
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  * [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:[email protected]).