Upload README.md with huggingface_hub

README.md

@@ -11,7 +11,7 @@ tags:
 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov8_seg/web-assets/model_demo.png)
 
 # YOLOv8-Segmentation: Optimized for Mobile Deployment
-## Real-time object segmentation optimized for mobile and edge
+## Real-time object segmentation optimized for mobile and edge by Ultralytics
 
 Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
 
@@ -25,7 +25,7 @@ More details on model performance across various devices, can be found
 
 - **Model Type:** Semantic segmentation
 - **Model Stats:**
-  - Model checkpoint: YOLOv8-Seg
+  - Model checkpoint: YOLOv8N-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 |  [YOLOv8-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 | 10.665 ms | 4 - 7 MB | FP16 | NPU |  [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) 
 
 
 ## Installation
@@ -94,16 +94,16 @@ python -m qai_hub_models.models.yolov8_seg.export
 ```
 Profile Job summary of YOLOv8-Segmentation
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 10.69 ms
-Estimated Peak Memory Range: 4.40-6.50 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 7.42 ms
+Estimated Peak Memory Range: 0.05-87.37 MB
 Compute Units: NPU (337) | Total (337)
 
 
 ```
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Yolo-v8-Segmentation/export.py)
+This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/YOLOv8-Segmentation/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -216,6 +216,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - 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]({deploy_license_url})
 
 ## References
 * [Real-Time Flying Object Detection with YOLOv8](https://arxiv.org/abs/2305.09972)