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README.md

@@ -16,7 +16,7 @@ tags:
 
 UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
 
-This model is an implementation of Unet-Segmentation found [here](https://github.com/milesial/Pytorch-UNet).
+This model is an implementation of Unet-Segmentation found [here]({source_repo}).
 This repository provides scripts to run Unet-Segmentation on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/unet_segmentation).
@@ -32,15 +32,32 @@ More details on model performance across various devices, can be found
   - Model size: 118 MB
   - Number of output classes: 2 (foreground / background)
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| Unet-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 153.929 ms | 6 - 442 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 151.064 ms | 10 - 30 MB | FP16 | NPU | [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) |
+| Unet-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 155.224 ms | 16 - 18 MB | FP16 | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
+| Unet-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 132.249 ms | 6 - 391 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 132.978 ms | 9 - 96 MB | FP16 | NPU | [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) |
+| Unet-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 134.367 ms | 0 - 402 MB | FP16 | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
+| Unet-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 142.642 ms | 6 - 442 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 136.843 ms | 10 - 11 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 147.599 ms | 6 - 442 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | SA8255 (Proxy) | SA8255P Proxy | QNN | 136.006 ms | 10 - 11 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 145.119 ms | 6 - 442 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | SA8775 (Proxy) | SA8775P Proxy | QNN | 143.044 ms | 10 - 11 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 157.28 ms | 6 - 457 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | SA8650 (Proxy) | SA8650P Proxy | QNN | 139.062 ms | 10 - 11 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 380.675 ms | 0 - 388 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 269.68 ms | 4 - 95 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 102.802 ms | 6 - 119 MB | FP16 | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
+| Unet-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 102.598 ms | 9 - 110 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 104.486 ms | 25 - 142 MB | FP16 | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
+| Unet-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 135.807 ms | 9 - 9 MB | FP16 | NPU | Use Export Script |
+| Unet-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 147.497 ms | 54 - 54 MB | FP16 | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-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 | 156.677 ms | 6 - 9 MB | FP16 | NPU |  [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 157.042 ms | 9 - 28 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
-
-
 
 ## Installation
 
@@ -95,16 +112,16 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.unet_segmentation.export
 ```
-
 ```
-Profile Job summary of Unet-Segmentation
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 135.62 ms
-Estimated Peak Memory Range: 9.39-9.39 MB
-Compute Units: NPU (52) | Total (52)
-
-
+Profiling Results
+------------------------------------------------------------
+Unet-Segmentation
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : TFLITE                 
+Estimated inference time (ms)   : 153.9                  
+Estimated peak memory usage (MB): [6, 442]               
+Total # Ops                     : 32                     
+Compute Unit(s)                 : NPU (32 ops)           
 ```
 
 
@@ -203,15 +220,19 @@ provides instructions on how to use the `.so` shared library  in an Android appl
 Get more details on Unet-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/unet_segmentation).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
+
 ## License
-- The license for the original implementation of Unet-Segmentation can be found
-  [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
+* The license for the original implementation of Unet-Segmentation can be found [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
+
+
 
 ## References
 * [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
 * [Source Model Implementation](https://github.com/milesial/Pytorch-UNet)
 
+
+
 ## 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:ai-hub-support@qti.qualcomm.com).