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README.md

@@ -31,14 +31,15 @@ More details on model performance across various devices, can be found
   - Input resolution: 513x513
   - Number of parameters: 5.80M
   - Model size: 6.04 MB
+  - Number of output classes: 21
 
 
 
 
 | 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 | 3.613 ms | 0 - 2 MB | INT8 | NPU |  [DeepLabV3-Plus-MobileNet-Quantized.tflite](https://huggingface.co/qualcomm/DeepLabV3-Plus-MobileNet-Quantized/blob/main/DeepLabV3-Plus-MobileNet-Quantized.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 5.334 ms | 1 - 7 MB | INT8 | NPU |  [DeepLabV3-Plus-MobileNet-Quantized.so](https://huggingface.co/qualcomm/DeepLabV3-Plus-MobileNet-Quantized/blob/main/DeepLabV3-Plus-MobileNet-Quantized.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 3.325 ms | 0 - 2 MB | INT8 | NPU |  [DeepLabV3-Plus-MobileNet-Quantized.tflite](https://huggingface.co/qualcomm/DeepLabV3-Plus-MobileNet-Quantized/blob/main/DeepLabV3-Plus-MobileNet-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 5.342 ms | 0 - 12 MB | INT8 | NPU |  [DeepLabV3-Plus-MobileNet-Quantized.so](https://huggingface.co/qualcomm/DeepLabV3-Plus-MobileNet-Quantized/blob/main/DeepLabV3-Plus-MobileNet-Quantized.so) 
 
 
 
@@ -101,8 +102,8 @@ python -m qai_hub_models.models.deeplabv3_plus_mobilenet_quantized.export
 Profile Job summary of DeepLabV3-Plus-MobileNet-Quantized
 --------------------------------------------------
 Device: SA8255 (Proxy) (13)
-Estimated Inference Time: 5.32 ms
-Estimated Peak Memory Range: 0.77-7.67 MB
+Estimated Inference Time: 5.33 ms
+Estimated Peak Memory Range: 0.77-13.24 MB
 Compute Units: NPU (100) | Total (100)