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README.md

@@ -14,7 +14,7 @@ tags:
 # Yolo-v7-Quantized: Optimized for Mobile Deployment
 ## Quantized real-time object detection optimized for mobile and edge
 
-YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the [COCO dataset](https://cocodataset.org/#home).
+YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the COCO dataset.
 
 This model is an implementation of Yolo-v7-Quantized found [here](https://github.com/WongKinYiu/yolov7/).
 This repository provides scripts to run Yolo-v7-Quantized on Qualcomm® devices.
@@ -34,8 +34,7 @@ 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 | 6.122 ms | 0 - 13 MB | INT8 | NPU |  [Yolo-v7-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-v7-Quantized/blob/main/Yolo-v7-Quantized.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 5.732 ms | 0 - 12 MB | INT8 | NPU |  [Yolo-v7-Quantized.so](https://huggingface.co/qualcomm/Yolo-v7-Quantized/blob/main/Yolo-v7-Quantized.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.575 ms | 0 - 2 MB | INT8 | NPU |  [Yolo-v7-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-v7-Quantized/blob/main/Yolo-v7-Quantized.tflite) 
 
 
 ## Installation
@@ -93,16 +92,6 @@ device. This script does the following:
 python -m qai_hub_models.models.yolov7_quantized.export
 ```
 
-```
-Profile Job summary of Yolo-v7-Quantized
---------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 5.98 ms
-Estimated Peak Memory Range: 4.71-14.69 MB
-Compute Units: NPU (220) | Total (220)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Yolo-v7-Quantized/export.py)