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README.md

@@ -34,10 +34,10 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.832 ms | 0 - 5 MB | FP16 | NPU |  [MediaPipePoseDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.234 ms | 0 - 2 MB | FP16 | NPU |  [MediaPipePoseLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.888 ms | 0 - 15 MB | FP16 | NPU |  [MediaPipePoseDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.so) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.299 ms | 0 - 15 MB | FP16 | NPU |  [MediaPipePoseLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.835 ms | 0 - 2 MB | FP16 | NPU |  [MediaPipePoseDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.206 ms | 0 - 2 MB | FP16 | NPU |  [MediaPipePoseLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.884 ms | 0 - 15 MB | FP16 | NPU |  [MediaPipePoseDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseDetector.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.297 ms | 0 - 15 MB | FP16 | NPU |  [MediaPipePoseLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Pose-Estimation/blob/main/MediaPipePoseLandmarkDetector.so) 
 
 
 ## Installation
@@ -94,37 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.mediapipe_pose.export
 ```
 
-```
-Profile Job summary of MediaPipePoseDetector
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.59 ms
-Estimated Peak Memory Range: 0.06-38.15 MB
-Compute Units: NPU (107) | Total (107)
-
-Profile Job summary of MediaPipePoseLandmarkDetector
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.90 ms
-Estimated Peak Memory Range: 0.02-83.68 MB
-Compute Units: NPU (230) | Total (230)
-
-Profile Job summary of MediaPipePoseDetector
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.64 ms
-Estimated Peak Memory Range: 0.00-40.35 MB
-Compute Units: NPU (140) | Total (140)
-
-Profile Job summary of MediaPipePoseLandmarkDetector
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.94 ms
-Estimated Peak Memory Range: 0.78-83.14 MB
-Compute Units: NPU (306) | Total (306)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/MediaPipe-Pose-Estimation/export.py)