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MediaPipe-Hand-Detection

Object Detection
PyTorch
TF Lite
ONNX
real_time
android
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  The MediaPipe Hand Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.
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- This model is an implementation of MediaPipe-Hand-Detection found [here](https://github.com/zmurez/MediaPipePyTorch/).
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  This repository provides scripts to run MediaPipe-Hand-Detection 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_hand).
@@ -31,17 +31,35 @@ More details on model performance across various devices, can be found
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  - Number of parameters (MediaPipeHandLandmarkDetector): 2.01M
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  - Model size (MediaPipeHandLandmarkDetector): 7.71 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 | 0.714 ms | 0 - 5 MB | FP16 | NPU | [MediaPipeHandDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.048 ms | 0 - 55 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.791 ms | 1 - 20 MB | FP16 | NPU | [MediaPipeHandDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.so)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.109 ms | 2 - 39 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.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_hand.export
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  ```
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-
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  ```
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- Profile Job summary of MediaPipeHandDetector
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 0.93 ms
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- Estimated Peak Memory Range: 0.75-0.75 MB
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- Compute Units: NPU (195) | Total (195)
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-
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- Profile Job summary of MediaPipeHandLandmarkDetector
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 1.34 ms
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- Estimated Peak Memory Range: 0.75-0.75 MB
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- Compute Units: NPU (208) | Total (208)
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-
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-
 
 
 
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  ```
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  Get more details on MediaPipe-Hand-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_hand).
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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-Hand-Detection 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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  * [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
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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 Hand Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.
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+ This model is an implementation of MediaPipe-Hand-Detection found [here]({source_repo}).
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  This repository provides scripts to run MediaPipe-Hand-Detection 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_hand).
 
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  - Number of parameters (MediaPipeHandLandmarkDetector): 2.01M
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  - Model size (MediaPipeHandLandmarkDetector): 7.71 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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+ | MediaPipeHandDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.704 ms | 0 - 4 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.16 ms | 0 - 17 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.onnx) |
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+ | MediaPipeHandDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.612 ms | 0 - 59 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.903 ms | 0 - 67 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.onnx) |
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+ | MediaPipeHandDetector | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.706 ms | 0 - 113 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.711 ms | 0 - 61 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.706 ms | 0 - 3 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.708 ms | 0 - 3 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.321 ms | 0 - 52 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.529 ms | 0 - 28 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.tflite) |
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+ | MediaPipeHandDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.878 ms | 0 - 32 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.onnx) |
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+ | MediaPipeHandDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.204 ms | 6 - 6 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandDetector.onnx) |
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+ | MediaPipeHandLandmarkDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.03 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.552 ms | 0 - 8 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.onnx) |
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+ | MediaPipeHandLandmarkDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.848 ms | 0 - 62 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.213 ms | 0 - 65 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.onnx) |
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+ | MediaPipeHandLandmarkDetector | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.003 ms | 0 - 171 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.008 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.004 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.035 ms | 0 - 1 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 2.59 ms | 0 - 55 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.585 ms | 0 - 32 MB | FP16 | NPU | [MediaPipe-Hand-Detection.tflite](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.tflite) |
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+ | MediaPipeHandLandmarkDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.068 ms | 0 - 37 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.onnx) |
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+ | MediaPipeHandLandmarkDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.641 ms | 8 - 8 MB | FP16 | NPU | [MediaPipe-Hand-Detection.onnx](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.mediapipe_hand.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ MediaPipeHandDetector
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 0.7
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+ Estimated peak memory usage (MB): [0, 4]
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+ Total # Ops : 149
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+ Compute Unit(s) : NPU (149 ops)
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+
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+ ------------------------------------------------------------
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+ MediaPipeHandLandmarkDetector
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 1.0
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+ Estimated peak memory usage (MB): [0, 1]
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+ Total # Ops : 158
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+ Compute Unit(s) : NPU (158 ops)
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  ```
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  Get more details on MediaPipe-Hand-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_hand).
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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-Hand-Detection 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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  * [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
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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).