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README.md

@@ -14,7 +14,7 @@ tags:
 # Unet-Segmentation: Optimized for Mobile Deployment
 ## Real-time segmentation optimized for mobile and edge
 
-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.
+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 repository provides scripts to run Unet-Segmentation on Qualcomm® devices.
@@ -30,14 +30,15 @@ More details on model performance across various devices, can be found
   - Input resolution: 224x224
   - Number of parameters: 31.0M
   - Model size: 118 MB
+  - Number of output classes: 2 (foreground / background)
 
 
 
 
 | 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 | 160.376 ms | 6 - 442 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 | 155.942 ms | 9 - 27 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 162.955 ms | 8 - 444 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 | 151.842 ms | 10 - 26 MB | FP16 | NPU |  [Unet-Segmentation.so](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.so) 
 
 
 
@@ -98,9 +99,9 @@ python -m qai_hub_models.models.unet_segmentation.export
 ```
 Profile Job summary of Unet-Segmentation
 --------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 133.37 ms
-Estimated Peak Memory Range: 9.39-9.39 MB
+Device: SA8255 (Proxy) (13)
+Estimated Inference Time: 157.83 ms
+Estimated Peak Memory Range: 9.41-27.04 MB
 Compute Units: NPU (51) | Total (51)