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README.md

@@ -32,10 +32,12 @@ More details on model performance across various devices, can be found
   - Model size: 159 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 | 134.142 ms | 1 - 4 MB | FP16 | NPU |  [DETR-ResNet50-DC5.tflite](https://huggingface.co/qualcomm/DETR-ResNet50-DC5/blob/main/DETR-ResNet50-DC5.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 132.937 ms | 0 - 52 MB | FP16 | NPU |  [DETR-ResNet50-DC5.so](https://huggingface.co/qualcomm/DETR-ResNet50-DC5/blob/main/DETR-ResNet50-DC5.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 133.335 ms | 0 - 4 MB | FP16 | NPU |  [DETR-ResNet50-DC5.tflite](https://huggingface.co/qualcomm/DETR-ResNet50-DC5/blob/main/DETR-ResNet50-DC5.tflite) 
+
 
 
 ## Installation
@@ -93,19 +95,11 @@ device. This script does the following:
 python -m qai_hub_models.models.detr_resnet50_dc5.export
 ```
 
-```
-Profile Job summary of DETR-ResNet50-DC5
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 165.65 ms
-Estimated Peak Memory Range: 2.64-2.64 MB
-Compute Units: NPU (863) | Total (863)
 
 
-```
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/DETR-ResNet50-DC5/export.py)
+This [export script](https://aihub.qualcomm.com/models/detr_resnet50_dc5/qai_hub_models/models/DETR-ResNet50-DC5/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:
 
@@ -182,6 +176,7 @@ 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.
@@ -218,7 +213,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of DETR-ResNet50-DC5 can be found
   [here](https://github.com/facebookresearch/detr/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
 * [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872)