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Shufflenet-v2

Image Classification
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ONNX
android
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  ShufflenetV2 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 Shufflenet-v2 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py).
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  This repository provides scripts to run Shufflenet-v2 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/shufflenet_v2).
@@ -32,15 +32,32 @@ More details on model performance across various devices, can be found
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  - Number of parameters: 1.36M
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  - Model size: 5.25 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 | 1.21 ms | 0 - 4 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.775 ms | 1 - 6 MB | FP16 | NPU | [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.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.shufflenet_v2.export
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  ```
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-
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  ```
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- Profile Job summary of Shufflenet-v2
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 0.88 ms
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- Estimated Peak Memory Range: 0.57-0.57 MB
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- Compute Units: NPU (158) | Total (158)
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-
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  ```
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  Get more details on Shufflenet-v2's performance across various devices [here](https://aihub.qualcomm.com/models/shufflenet_v2).
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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 Shufflenet-v2 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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  * [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.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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  ShufflenetV2 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 Shufflenet-v2 found [here]({source_repo}).
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  This repository provides scripts to run Shufflenet-v2 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/shufflenet_v2).
 
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  - Number of parameters: 1.36M
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  - Model size: 5.25 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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+ | Shufflenet-v2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.201 ms | 0 - 1 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.774 ms | 0 - 15 MB | FP16 | NPU | [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) |
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+ | Shufflenet-v2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.128 ms | 0 - 2 MB | FP16 | NPU | [Shufflenet-v2.onnx](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.onnx) |
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+ | Shufflenet-v2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.975 ms | 0 - 38 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.518 ms | 1 - 13 MB | FP16 | NPU | [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) |
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+ | Shufflenet-v2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.728 ms | 0 - 41 MB | FP16 | NPU | [Shufflenet-v2.onnx](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.onnx) |
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+ | Shufflenet-v2 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.197 ms | 0 - 1 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.732 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.2 ms | 0 - 2 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.741 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.196 ms | 0 - 1 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | SA8775 (Proxy) | SA8775P Proxy | QNN | 0.733 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.204 ms | 0 - 1 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.741 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.315 ms | 0 - 39 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 0.885 ms | 1 - 14 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.803 ms | 0 - 21 MB | FP16 | NPU | [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) |
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+ | Shufflenet-v2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.407 ms | 0 - 10 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.786 ms | 0 - 23 MB | FP16 | NPU | [Shufflenet-v2.onnx](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.onnx) |
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+ | Shufflenet-v2 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.894 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | Shufflenet-v2 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.124 ms | 3 - 3 MB | FP16 | NPU | [Shufflenet-v2.onnx](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.shufflenet_v2.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ Shufflenet-v2
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 1.2
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+ Estimated peak memory usage (MB): [0, 1]
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+ Total # Ops : 204
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+ Compute Unit(s) : NPU (204 ops)
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  ```
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  Get more details on Shufflenet-v2's performance across various devices [here](https://aihub.qualcomm.com/models/shufflenet_v2).
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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 Shufflenet-v2 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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+
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  ## References
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  * [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.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]).