v0.34.0

See https://github.com/quic/ai-hub-models/releases/v0.34.0 for changelog.

README.md

@@ -23,6 +23,7 @@ More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/detr_resnet50_dc5).
 
 
+
 ### Model Details
 
 - **Model Type:** Model_use_case.object_detection
@@ -107,17 +108,7 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.detr_resnet50_dc5.export
 ```
-```
-Profiling Results
-------------------------------------------------------------
-DETR-ResNet50-DC5
-Device                          : cs_8275 (ANDROID 14)                 
-Runtime                         : QNN_DLC                              
-Estimated inference time (ms)   : 167.8                                
-Estimated peak memory usage (MB): [0, 256]                             
-Total # Ops                     : 827                                  
-Compute Unit(s)                 : npu (827 ops) gpu (0 ops) cpu (0 ops)
-```
+
 
 
 ## How does this work?

precompiled/qualcomm-snapdragon-x-elite/DETR-ResNet50-DC5.onnx.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:71897f8215bdc06928301ee7fd6b678acc67872bbec593abf87b54f234016f4d
+oid sha256:1ce95fd9977070e5e30a00a424f9e50b61bea511b85cd1492e047c9d1ac5dea4
 size 78410589

precompiled/qualcomm-snapdragon-x-elite/sdk_versions.yml

@@ -0,0 +1,5 @@
+sdk_versions:
+  qnn_context_binary:
+    qairt: 2.34.2.250528164111_119506
+  precompiled_qnn_onnx:
+    qairt: 2.33.2.250410134701_117956