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README.md

@@ -32,13 +32,14 @@ More details on model performance across various devices, can be found
   - Input resolution: 2048x1024
   - Number of parameters: 18.0M
   - Model size: 17.5 MB
+  - Number of output classes: 19
 
 
 
 
 | 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 | 7.131 ms | 0 - 2 MB | INT8 | NPU |  [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.719 ms | 2 - 10 MB | INT8 | NPU |  [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) 
 
 
 
@@ -97,16 +98,6 @@ device. This script does the following:
 python -m qai_hub_models.models.ffnet_54s_quantized.export
 ```
 
-```
-Profile Job summary of FFNet-54S-Quantized
---------------------------------------------------
-Device: RB5 (Proxy) (12)
-Estimated Inference Time: 198.80 ms
-Estimated Peak Memory Range: 0.84-2.88 MB
-Compute Units: NPU (118) | Total (118)
-
-
-```