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Posenet-Mobilenet

Keypoint Detection
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TF Lite
ONNX
android
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  Posenet performs pose estimation on human images.
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- This model is an implementation of Posenet-Mobilenet found [here](https://github.com/rwightman/posenet-pytorch).
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  This repository provides scripts to run Posenet-Mobilenet 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/posenet_mobilenet).
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  - Number of parameters: 3.31M
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  - Model size: 12.7 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 | 1.367 ms | 0 - 7 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.444 ms | 0 - 13 MB | FP16 | NPU | [Posenet-Mobilenet.so](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.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.posenet_mobilenet.export
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  ```
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-
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  ```
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- Profile Job summary of Posenet-Mobilenet
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 1.57 ms
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- Estimated Peak Memory Range: 1.52-1.52 MB
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- Compute Units: NPU (69) | Total (69)
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-
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  ```
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  Get more details on Posenet-Mobilenet's performance across various devices [here](https://aihub.qualcomm.com/models/posenet_mobilenet).
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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 Posenet-Mobilenet can be found
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- [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt).
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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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  * [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225)
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  * [Source Model Implementation](https://github.com/rwightman/posenet-pytorch)
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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:ai-hub-support@qti.qualcomm.com).
 
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  Posenet performs pose estimation on human images.
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+ This model is an implementation of Posenet-Mobilenet found [here]({source_repo}).
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  This repository provides scripts to run Posenet-Mobilenet 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/posenet_mobilenet).
 
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  - Number of parameters: 3.31M
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  - Model size: 12.7 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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+ | Posenet-Mobilenet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.375 ms | 0 - 32 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.442 ms | 0 - 12 MB | FP16 | NPU | [Posenet-Mobilenet.so](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.so) |
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+ | Posenet-Mobilenet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.894 ms | 0 - 7 MB | FP16 | NPU | [Posenet-Mobilenet.onnx](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.onnx) |
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+ | Posenet-Mobilenet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.102 ms | 0 - 40 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.157 ms | 2 - 18 MB | FP16 | NPU | [Posenet-Mobilenet.so](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.so) |
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+ | Posenet-Mobilenet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.493 ms | 0 - 45 MB | FP16 | NPU | [Posenet-Mobilenet.onnx](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.onnx) |
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+ | Posenet-Mobilenet | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.396 ms | 0 - 1 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.387 ms | 2 - 3 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.369 ms | 0 - 2 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.396 ms | 2 - 3 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.364 ms | 0 - 9 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.39 ms | 2 - 3 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.372 ms | 0 - 2 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.399 ms | 2 - 3 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 2.195 ms | 0 - 41 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 2.293 ms | 2 - 21 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.963 ms | 0 - 22 MB | FP16 | NPU | [Posenet-Mobilenet.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.tflite) |
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+ | Posenet-Mobilenet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.077 ms | 2 - 15 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.076 ms | 0 - 24 MB | FP16 | NPU | [Posenet-Mobilenet.onnx](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.onnx) |
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+ | Posenet-Mobilenet | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.556 ms | 2 - 2 MB | FP16 | NPU | Use Export Script |
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+ | Posenet-Mobilenet | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.147 ms | 7 - 7 MB | FP16 | NPU | [Posenet-Mobilenet.onnx](https://huggingface.co/qualcomm/Posenet-Mobilenet/blob/main/Posenet-Mobilenet.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.posenet_mobilenet.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ Posenet-Mobilenet
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 1.4
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+ Estimated peak memory usage (MB): [0, 32]
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+ Total # Ops : 41
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+ Compute Unit(s) : NPU (41 ops)
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  ```
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  Get more details on Posenet-Mobilenet's performance across various devices [here](https://aihub.qualcomm.com/models/posenet_mobilenet).
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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 Posenet-Mobilenet can be found [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt).
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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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  * [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225)
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  * [Source Model Implementation](https://github.com/rwightman/posenet-pytorch)
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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:ai-hub-support@qti.qualcomm.com).