v0.58.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.58.0 for changelog.

README.md

@@ -14,7 +14,7 @@ pipeline_tag: image-segmentation
 Mask R-CNN is a machine learning model that extends Faster R-CNN to perform instance segmentation by detecting objects in an image while simultaneously generating a high-quality segmentation mask for each instance. It adds a branch for predicting segmentation masks in parallel with the existing branch for bounding box recognition.
 
 This is based on the implementation of MaskRCNN found [here](https://github.com/pytorch/vision).
-This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/maskrcnn) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/maskrcnn) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
 
 Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
 
@@ -27,21 +27,21 @@ Below are pre-exported model assets ready for deployment.
 
 | Runtime | Precision | Chipset | SDK Versions | Download |
 |---|---|---|---|---|
-| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/maskrcnn/releases/v0.57.3/maskrcnn-qnn_dlc-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/maskrcnn/releases/v0.58.0/maskrcnn-qnn_dlc-float.zip)
 
 For more device-specific assets and performance metrics, visit **[MaskRCNN on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/maskrcnn)**.
 
 
 ### Option 2: Export with Custom Configurations
 
-Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/maskrcnn) Python library to compile and export the model with your own:
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/maskrcnn) Python library to compile and export the model with your own:
 - Custom weights (e.g., fine-tuned checkpoints)
 - Custom input shapes
 - Target device and runtime configurations
 
 This option is ideal if you need to customize the model beyond the default configuration provided here.
 
-See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/maskrcnn) for usage instructions.
+See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/maskrcnn) for usage instructions.
 
 ## Model Details
 
@@ -62,29 +62,29 @@ See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-m
 | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 70.051 ms | 7 - 2327 MB | NPU
 | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 152.938 ms | 8 - 2736 MB | NPU
 | proposal_generator | QNN_DLC | float | Qualcomm® QCS8275 | 354.153 ms | 2 - 1758 MB | NPU
-| proposal_generator | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 95.709 ms | 7 - 16 MB | NPU
+| proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 95.709 ms | 7 - 16 MB | NPU
 | proposal_generator | QNN_DLC | float | Qualcomm® QCS8450 | 152.938 ms | 8 - 2736 MB | NPU
 | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 53.463 ms | 0 - 1510 MB | NPU
 | proposal_generator | QNN_DLC | float | Qualcomm® SA8295P | 125.945 ms | 3 - 1432 MB | NPU
 | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 41.81 ms | 7 - 1789 MB | NPU
 | proposal_generator | QNN_DLC | float | Qualcomm® SA7255P | 354.153 ms | 2 - 1758 MB | NPU
-| proposal_generator | QNN_DLC | float | Qualcomm® QCS9075 | 118.978 ms | 7 - 71 MB | NPU
-| proposal_generator | QNN_DLC | float | Qualcomm® QCS8750 | 53.463 ms | 0 - 1510 MB | NPU
-| proposal_generator | QNN_DLC | float | Qualcomm® QCS7181 | 91.325 ms | 7 - 7 MB | NPU
+| proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 118.978 ms | 7 - 71 MB | NPU
+| proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 53.463 ms | 0 - 1510 MB | NPU
+| proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 91.325 ms | 7 - 7 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® X2 Elite | 98.085 ms | 52 - 52 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® X Elite | 250.621 ms | 52 - 52 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 177.887 ms | 13 - 860 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 312.774 ms | 39 - 960 MB | NPU
 | roi_head | QNN_DLC | float | Qualcomm® QCS8275 | 573.715 ms | 49 - 744 MB | NPU
-| roi_head | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 242.438 ms | 52 - 54 MB | NPU
+| roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 242.438 ms | 52 - 54 MB | NPU
 | roi_head | QNN_DLC | float | Qualcomm® QCS8450 | 312.774 ms | 39 - 960 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 129.478 ms | 39 - 730 MB | NPU
 | roi_head | QNN_DLC | float | Qualcomm® SA8295P | 325.17 ms | 49 - 846 MB | NPU
 | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 96.26 ms | 15 - 721 MB | NPU
 | roi_head | QNN_DLC | float | Qualcomm® SA7255P | 573.715 ms | 49 - 744 MB | NPU
-| roi_head | QNN_DLC | float | Qualcomm® QCS9075 | 324.226 ms | 52 - 106 MB | NPU
-| roi_head | QNN_DLC | float | Qualcomm® QCS8750 | 129.478 ms | 39 - 730 MB | NPU
-| roi_head | QNN_DLC | float | Qualcomm® QCS7181 | 250.621 ms | 52 - 52 MB | NPU
+| roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 324.226 ms | 52 - 106 MB | NPU
+| roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 129.478 ms | 39 - 730 MB | NPU
+| roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 250.621 ms | 52 - 52 MB | NPU
 
 ## License
 * The license for the original implementation of MaskRCNN can be found

release_assets.json

@@ -1,5 +1,5 @@
 {
-    "version": "0.57.3",
+    "version": "0.58.0",
     "precisions": {
         "float": {
             "universal_assets": {
@@ -7,7 +7,7 @@
                     "tool_versions": {
                         "qairt": "2.45.0.260326154327"
                     },
-                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/maskrcnn/releases/v0.57.3/maskrcnn-qnn_dlc-float.zip"
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/maskrcnn/releases/v0.58.0/maskrcnn-qnn_dlc-float.zip"
                 }
             }
         }