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README.md

@@ -15,7 +15,7 @@ tags:
 
 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 YOLOv8-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
+This model is an implementation of YOLOv8-Segmentation found [here]({source_repo}).
 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).
@@ -31,15 +31,32 @@ More details on model performance across various devices, can be found
   - Model size: 13.2 MB
   - Number of output classes: 80
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 6.541 ms | 4 - 6 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 6.409 ms | 4 - 14 MB | FP16 | NPU | [YOLOv8-Segmentation.so](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.so) |
+| YOLOv8-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.616 ms | 13 - 22 MB | FP16 | NPU | [YOLOv8-Segmentation.onnx](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.onnx) |
+| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.861 ms | 3 - 112 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.775 ms | 5 - 62 MB | FP16 | NPU | [YOLOv8-Segmentation.so](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.so) |
+| YOLOv8-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 5.228 ms | 18 - 133 MB | FP16 | NPU | [YOLOv8-Segmentation.onnx](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.onnx) |
+| YOLOv8-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 6.414 ms | 0 - 19 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 6.283 ms | 5 - 6 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 6.446 ms | 0 - 200 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | SA8255 (Proxy) | SA8255P Proxy | QNN | 6.278 ms | 5 - 10 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 6.49 ms | 4 - 7 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | SA8775 (Proxy) | SA8775P Proxy | QNN | 6.277 ms | 5 - 12 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 6.533 ms | 4 - 14 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | SA8650 (Proxy) | SA8650P Proxy | QNN | 6.38 ms | 5 - 6 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 9.61 ms | 5 - 103 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 9.155 ms | 5 - 44 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 4.508 ms | 4 - 75 MB | FP16 | NPU | [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) |
+| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.685 ms | 5 - 57 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.821 ms | 0 - 71 MB | FP16 | NPU | [YOLOv8-Segmentation.onnx](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.onnx) |
+| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.215 ms | 5 - 5 MB | FP16 | NPU | Use Export Script |
+| YOLOv8-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.647 ms | 17 - 17 MB | FP16 | NPU | [YOLOv8-Segmentation.onnx](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.onnx) |
 
 
 
-| 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 | 6.418 ms | 4 - 7 MB | FP16 | NPU |  [YOLOv8-Segmentation.tflite](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 6.398 ms | 7 - 17 MB | FP16 | NPU |  [YOLOv8-Segmentation.so](https://huggingface.co/qualcomm/YOLOv8-Segmentation/blob/main/YOLOv8-Segmentation.so) 
-
-
 
 ## Installation
 
@@ -95,16 +112,16 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.yolov8_seg.export
 ```
-
 ```
-Profile Job summary of YOLOv8-Segmentation
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 6.42 ms
-Estimated Peak Memory Range: 4.70-4.70 MB
-Compute Units: NPU (333) | Total (333)
-
-
+Profiling Results
+------------------------------------------------------------
+YOLOv8-Segmentation
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : TFLITE                 
+Estimated inference time (ms)   : 6.5                    
+Estimated peak memory usage (MB): [4, 6]                 
+Total # Ops                     : 338                    
+Compute Unit(s)                 : NPU (338 ops)          
 ```
 
 
@@ -203,15 +220,19 @@ provides instructions on how to use the `.so` shared library  in an Android appl
 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 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://github.com/ultralytics/ultralytics/blob/main/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](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
+
+
 
 ## References
 * [Ultralytics YOLOv8 Docs: Instance Segmentation](https://docs.ultralytics.com/tasks/segment/)
 * [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment)
 
+
+
 ## Community
 * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).