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README.md

@@ -30,9 +30,12 @@ More details on model performance across various devices, can be found
   - Model size: 151 MB
 
 
+
+
 | 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 | 290.505 ms | 4 - 173 MB | FP16 | GPU |  [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 292.98 ms | 2 - 143 MB | FP16 | GPU |  [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) 
+
 
 
 ## Installation
@@ -89,9 +92,21 @@ device. This script does the following:
 python -m qai_hub_models.models.deeplabv3_resnet50.export
 ```
 
+```
+Profile Job summary of DeepLabV3-ResNet50
+--------------------------------------------------
+Device: QCS8550 (Proxy) (12)
+Estimated Inference Time: 291.24 ms
+Estimated Peak Memory Range: 5.22-174.24 MB
+Compute Units: GPU (95) | Total (95)
+
+
+```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/DeepLabV3-ResNet50/export.py)
+This [export script](https://aihub.qualcomm.com/models/deeplabv3_resnet50/qai_hub_models/models/DeepLabV3-ResNet50/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -168,6 +183,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
+
 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -204,7 +220,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of DeepLabV3-ResNet50 can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)