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@@ -34,10 +34,10 @@ More details on model performance across various devices, can be found
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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 | 0.762 ms | 0 - 3 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 | 1.017 ms | 0 - 2 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 | 0.82 ms | 1 - 6 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 | 1.088 ms | 1 - 51 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.so)
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  ## Installation
@@ -45,11 +45,10 @@ More details on model performance across various devices, can be found
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  This model can be installed as a Python package via pip.
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  ```bash
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- pip install "qai-hub-models[mediapipe_hand]"
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  ```
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-
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  ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
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  Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
@@ -98,31 +97,31 @@ python -m qai_hub_models.models.mediapipe_hand.export
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  ```
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  Profile Job summary of MediaPipeHandDetector
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  --------------------------------------------------
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- Device: Samsung Galaxy S23 Ultra (13)
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- Estimated Inference Time: 0.76 ms
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- Estimated Peak Memory Range: 0.01-3.13 MB
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  Compute Units: NPU (151) | Total (151)
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  Profile Job summary of MediaPipeHandLandmarkDetector
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  --------------------------------------------------
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- Device: Samsung Galaxy S23 Ultra (13)
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- Estimated Inference Time: 1.02 ms
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- Estimated Peak Memory Range: 0.02-2.30 MB
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  Compute Units: NPU (158) | Total (158)
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  Profile Job summary of MediaPipeHandDetector
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  --------------------------------------------------
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- Device: Samsung Galaxy S23 Ultra (13)
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- Estimated Inference Time: 0.82 ms
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- Estimated Peak Memory Range: 0.77-5.97 MB
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- Compute Units: NPU (196) | Total (196)
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  Profile Job summary of MediaPipeHandLandmarkDetector
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  --------------------------------------------------
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- Device: Samsung Galaxy S23 Ultra (13)
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- Estimated Inference Time: 1.09 ms
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- Estimated Peak Memory Range: 0.55-51.09 MB
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- Compute Units: NPU (209) | Total (209)
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  ```
@@ -227,7 +226,7 @@ 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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  | 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 | 0.765 ms | 0 - 12 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 | 1.047 ms | 0 - 2 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 | 0.763 ms | 0 - 2 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 | 0.996 ms | 0 - 10 MB | FP16 | NPU | [MediaPipeHandLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Hand-Detection/blob/main/MediaPipeHandLandmarkDetector.so)
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  ## Installation
 
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  This model can be installed as a Python package via pip.
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  ```bash
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+ pip install qai-hub-models
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  ```
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  ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
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  Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
 
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  ```
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  Profile Job summary of MediaPipeHandDetector
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  --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.57 ms
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+ Estimated Peak Memory Range: 0.01-49.27 MB
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  Compute Units: NPU (151) | Total (151)
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  Profile Job summary of MediaPipeHandLandmarkDetector
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  --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.75 ms
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+ Estimated Peak Memory Range: 0.02-51.85 MB
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  Compute Units: NPU (158) | Total (158)
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  Profile Job summary of MediaPipeHandDetector
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  --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.55 ms
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+ Estimated Peak Memory Range: 0.01-49.65 MB
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+ Compute Units: NPU (151) | Total (151)
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  Profile Job summary of MediaPipeHandLandmarkDetector
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  --------------------------------------------------
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+ Device: Samsung Galaxy S24 (14)
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+ Estimated Inference Time: 0.75 ms
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+ Estimated Peak Memory Range: 0.01-51.44 MB
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+ Compute Units: NPU (158) | Total (158)
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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
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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]({deploy_license_url})
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  ## References
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  * [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)