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MediaPipe-Pose-Estimation

Keypoint Detection
PyTorch
TF Lite
ONNX
real_time
android
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  The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of poses in an image.
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- This model is an implementation of MediaPipe-Pose-Estimation found [here](https://github.com/zmurez/MediaPipePyTorch/).
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  This repository provides scripts to run MediaPipe-Pose-Estimation 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/mediapipe_pose).
@@ -31,17 +31,35 @@ More details on model performance across various devices, can be found
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  - Number of parameters (MediaPipePoseLandmarkDetector): 3.37M
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  - Model size (MediaPipePoseLandmarkDetector): 12.9 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 | 0.774 ms | 0 - 2 MB | FP16 | NPU | [MediaPipePoseDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.832 ms | 0 - 2 MB | FP16 | NPU | [MediaPipePoseLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.838 ms | 0 - 6 MB | FP16 | NPU | [MediaPipePoseDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.so)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.915 ms | 0 - 38 MB | FP16 | NPU | [MediaPipePoseLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.so)
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-
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-
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  ## Installation
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@@ -96,23 +114,25 @@ device. This script does the following:
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  ```bash
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  python -m qai_hub_models.models.mediapipe_pose.export
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  ```
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-
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  ```
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- Profile Job summary of MediaPipePoseDetector
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 0.98 ms
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- Estimated Peak Memory Range: 0.45-0.45 MB
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- Compute Units: NPU (138) | Total (138)
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-
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- Profile Job summary of MediaPipePoseLandmarkDetector
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 1.12 ms
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- Estimated Peak Memory Range: 0.75-0.75 MB
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- Compute Units: NPU (290) | Total (290)
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-
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-
 
 
 
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  ```
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  Get more details on MediaPipe-Pose-Estimation's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_pose).
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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 MediaPipe-Pose-Estimation can be found
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- [here](https://github.com/zmurez/MediaPipePyTorch/blob/master/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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  * [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/abs/2006.10204)
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  * [Source Model Implementation](https://github.com/zmurez/MediaPipePyTorch/)
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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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  The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of poses in an image.
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+ This model is an implementation of MediaPipe-Pose-Estimation found [here]({source_repo}).
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  This repository provides scripts to run MediaPipe-Pose-Estimation 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/mediapipe_pose).
 
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  - Number of parameters (MediaPipePoseLandmarkDetector): 3.37M
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  - Model size (MediaPipePoseLandmarkDetector): 12.9 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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+ | MediaPipePoseDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.774 ms | 0 - 5 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.009 ms | 0 - 4 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.onnx) |
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+ | MediaPipePoseDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.669 ms | 0 - 47 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.898 ms | 0 - 50 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.onnx) |
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+ | MediaPipePoseDetector | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.774 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.777 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.779 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.774 ms | 0 - 2 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.892 ms | 0 - 42 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.457 ms | 0 - 24 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) |
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+ | MediaPipePoseDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.755 ms | 0 - 26 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.onnx) |
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+ | MediaPipePoseDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.057 ms | 3 - 3 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.onnx) |
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+ | MediaPipePoseLandmarkDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.831 ms | 0 - 6 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.315 ms | 0 - 9 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.onnx) |
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+ | MediaPipePoseLandmarkDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.705 ms | 0 - 90 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.012 ms | 0 - 96 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.onnx) |
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+ | MediaPipePoseLandmarkDetector | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.818 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.841 ms | 0 - 8 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.826 ms | 0 - 2 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.846 ms | 0 - 5 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.814 ms | 0 - 79 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.549 ms | 0 - 35 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) |
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+ | MediaPipePoseLandmarkDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.916 ms | 0 - 43 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.onnx) |
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+ | MediaPipePoseLandmarkDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.382 ms | 8 - 8 MB | FP16 | NPU | [MediaPipe-Pose-Estimation.onnx](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.mediapipe_pose.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ MediaPipePoseDetector
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 0.8
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+ Estimated peak memory usage (MB): [0, 5]
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+ Total # Ops : 106
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+ Compute Unit(s) : NPU (106 ops)
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+
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+ ------------------------------------------------------------
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+ MediaPipePoseLandmarkDetector
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 0.8
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+ Estimated peak memory usage (MB): [0, 6]
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+ Total # Ops : 219
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+ Compute Unit(s) : NPU (219 ops)
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  ```
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  Get more details on MediaPipe-Pose-Estimation's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_pose).
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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 MediaPipe-Pose-Estimation can be found [here](https://github.com/zmurez/MediaPipePyTorch/blob/master/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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  * [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/abs/2006.10204)
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  * [Source Model Implementation](https://github.com/zmurez/MediaPipePyTorch/)
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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).