v0.19.1

See https://github.com/quic/ai-hub-models/releases/v0.19.1 for changelog.

Signed-off-by: QAIHM Team <[email protected]>

qai_hub_models/_version.py

@@ -2,4 +2,4 @@
 # Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
 # SPDX-License-Identifier: BSD-3-Clause
 # ---------------------------------------------------------------------
-__version__ = "0.19.0"
+__version__ = "0.19.1"

qai_hub_models/models/conditional_detr_resnet50/README.md

@@ -46,12 +46,12 @@ script requires access to Deployment instructions for Qualcomm® AI Hub.
 
 ## License
 * The license for the original implementation of Conditional-DETR-ResNet50 can be found
-  [here](https://arxiv.org/abs/2108.06152).
+  [here](https://github.com/huggingface/transformers/blob/main/LICENSE).
 * 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
-* [Conditional {DETR} for Fast Training Convergence](Conditional {DETR} for Fast Training Convergence)
+* [Conditional {DETR} for Fast Training Convergence](https://arxiv.org/abs/2108.06152)
 * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr)
 
 

qai_hub_models/models/conditional_detr_resnet50/info.yaml

@@ -8,16 +8,16 @@ domain: Computer Vision
 description: DETR is a machine learning model that can detect objects (trained on
   COCO dataset).
 use_case: Object Detection
-research_paper: Conditional {DETR} for Fast Training Convergence
+research_paper: https://arxiv.org/abs/2108.06152
 research_paper_title: Conditional {DETR} for Fast Training Convergence
-license: https://arxiv.org/abs/2108.06152
+license: https://github.com/huggingface/transformers/blob/main/LICENSE
 deploy_license: https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf
 source_repo: https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr
 technical_details:
-  Model checkpoint:
-  Input resolution:
-  Number of parameters:
-  Model size:
+  Model checkpoint: ResNet50
+  Input resolution: 480x480
+  Number of parameters: 44M
+  Model size: 165 MB
 applicable_scenarios:
   - Factory Automation
   - Robotic Navigation

qai_hub_models/models/mobile_vit/test.py

@@ -100,6 +100,7 @@ def test_task():
     )
 
 
+@pytest.mark.skip("TODO: #13142 fails to trace.")
 @pytest.mark.trace
 def test_trace():
     run_classifier_trace_test(MobileVIT.from_pretrained())