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_resnet101_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_resnet101_dc5.export
 ```
-```
-Profiling Results
-------------------------------------------------------------
-DETR-ResNet101-DC5
-Device                          : cs_8275 (ANDROID 14)                 
-Runtime                         : QNN_DLC                              
-Estimated inference time (ms)   : 214.9                                
-Estimated peak memory usage (MB): [0, 280]                             
-Total # Ops                     : 946                                  
-Compute Unit(s)                 : npu (946 ops) gpu (0 ops) cpu (0 ops)
-```
+
 
 
 ## How does this work?

precompiled/qualcomm-snapdragon-x-elite/DETR-ResNet101-DC5.onnx.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:51ff8049b6c1a425384e3a8e2f65875cc79bac6365b05fe17d9a43408c32125f
-size 114056941
+oid sha256:63756a3daf8fec7d2a0bfc3bf3245af3ac6e86a73de0f153c184dfe0aa0efa95
+size 114056942

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