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QuickSRNetSmall

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  QuickSRNet Small is designed for upscaling images on mobile platforms to sharpen in real-time.
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- This model is an implementation of QuickSRNetSmall found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/quicksrnet).
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  This repository provides scripts to run QuickSRNetSmall 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/quicksrnetsmall).
@@ -29,15 +29,32 @@ More details on model performance across various devices, can be found
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  - Number of parameters: 27.2K
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  - Model size: 110 KB
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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 | 1.298 ms | 7 - 75 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.01 ms | 0 - 62 MB | FP16 | NPU | [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so)
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-
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-
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.quicksrnetsmall.export
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  ```
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-
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  ```
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- Profile Job summary of QuickSRNetSmall
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 0.94 ms
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- Estimated Peak Memory Range: 0.20-0.20 MB
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- Compute Units: NPU (11) | Total (11)
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-
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  ```
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  Get more details on QuickSRNetSmall's performance across various devices [here](https://aihub.qualcomm.com/models/quicksrnetsmall).
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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 QuickSRNetSmall can be found
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- [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
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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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  * [QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms](https://arxiv.org/abs/2303.04336)
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  * [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/quicksrnet)
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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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  QuickSRNet Small is designed for upscaling images on mobile platforms to sharpen in real-time.
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+ This model is an implementation of QuickSRNetSmall found [here]({source_repo}).
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  This repository provides scripts to run QuickSRNetSmall 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/quicksrnetsmall).
 
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  - Number of parameters: 27.2K
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  - Model size: 110 KB
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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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+ | QuickSRNetSmall | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.34 ms | 0 - 8 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.061 ms | 0 - 7 MB | FP16 | NPU | [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) |
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+ | QuickSRNetSmall | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.44 ms | 0 - 2 MB | FP16 | NPU | [QuickSRNetSmall.onnx](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.onnx) |
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+ | QuickSRNetSmall | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.83 ms | 0 - 20 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.638 ms | 0 - 12 MB | FP16 | NPU | [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) |
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+ | QuickSRNetSmall | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.997 ms | 0 - 21 MB | FP16 | NPU | [QuickSRNetSmall.onnx](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.onnx) |
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+ | QuickSRNetSmall | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.36 ms | 0 - 1 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.863 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.413 ms | 0 - 22 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.877 ms | 0 - 4 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.361 ms | 0 - 1 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | SA8775 (Proxy) | SA8775P Proxy | QNN | 0.863 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.316 ms | 0 - 1 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.872 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 2.807 ms | 0 - 20 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.118 ms | 0 - 12 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.746 ms | 0 - 14 MB | FP16 | NPU | [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) |
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+ | QuickSRNetSmall | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.663 ms | 0 - 9 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.968 ms | 0 - 14 MB | FP16 | NPU | [QuickSRNetSmall.onnx](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.onnx) |
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+ | QuickSRNetSmall | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.942 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
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+ | QuickSRNetSmall | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.468 ms | 9 - 9 MB | FP16 | NPU | [QuickSRNetSmall.onnx](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.quicksrnetsmall.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ QuickSRNetSmall
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 1.3
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+ Estimated peak memory usage (MB): [0, 8]
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+ Total # Ops : 11
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+ Compute Unit(s) : NPU (8 ops) CPU (3 ops)
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  ```
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  Get more details on QuickSRNetSmall's performance across various devices [here](https://aihub.qualcomm.com/models/quicksrnetsmall).
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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 QuickSRNetSmall can be found [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
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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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  * [QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms](https://arxiv.org/abs/2303.04336)
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  * [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/quicksrnet)
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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]).