Update README.md

README.md

@@ -10,13 +10,13 @@ tags:
 
 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov8_seg/web-assets/model_demo.png)
 
-# Yolo-v8-Segmentation: Optimized for Mobile Deployment
+# YOLOv8-Segmentation: Optimized for Mobile Deployment
 ## Real-time object segmentation optimized for mobile and edge
 
-YoloV8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
+Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
 
-This model is an implementation of Yolo-v8-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
-This repository provides scripts to run Yolo-v8-Segmentation on Qualcomm® devices.
+This model is an implementation of YOLOv8-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
+This repository provides scripts to run YOLOv8-Segmentation on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/yolov8_seg).
 
@@ -25,7 +25,7 @@ More details on model performance across various devices, can be found
 
 - **Model Type:** Semantic segmentation
 - **Model Stats:**
-  - Model checkpoint: YoloV8N-Seg
+  - Model checkpoint: YOLOv8-Seg
   - Input resolution: 640x640
   - Number of parameters: 3.43M
   - Model size: 13.2 MB
@@ -33,7 +33,7 @@ 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 | 10.686 ms | 4 - 7 MB | FP16 | NPU |  [Yolo-v8-Segmentation.tflite](https://huggingface.co/qualcomm/Yolo-v8-Segmentation/blob/main/Yolo-v8-Segmentation.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 10.686 ms | 4 - 7 MB | FP16 | NPU |  [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/Yolo-v8-Segmentation/blob/main/Yolo-v8-Segmentation.tflite) 
 
 
 ## Installation
@@ -92,7 +92,7 @@ python -m qai_hub_models.models.yolov8_seg.export
 ```
 
 ```
-Profile Job summary of Yolo-v8-Segmentation
+Profile Job summary of YOLOv8-Segmentation
 --------------------------------------------------
 Device: Samsung Galaxy S23 Ultra (13)
 Estimated Inference Time: 10.69 ms
@@ -210,13 +210,12 @@ provides instructions on how to use the `.so` shared library  in an Android appl
 
 
 ## View on Qualcomm® AI Hub
-Get more details on Yolo-v8-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/yolov8_seg).
+Get more details on YOLOv8-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/yolov8_seg).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
 ## License
-- The license for the original implementation of Yolo-v8-Segmentation can be found
+- The license for the original implementation of YOLOv8-Segmentation can be found
   [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
-- 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).
 
 ## References
 * [Real-Time Flying Object Detection with YOLOv8](https://arxiv.org/abs/2305.09972)