v0.52.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.52.0 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/mlcommons/inference/blob/33894a19c4af6207f7cfdda75f84570f04836de5/LICENSE.md.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- android
+pipeline_tag: object-detection
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/web-assets/model_demo.png)
+
+# ResNet34-SSD: Optimized for Qualcomm Devices
+
+ResNet34-SSD is a single-stage object detection model that integrates the ResNet34 backbone with the SSD (Single Shot MultiBox Detector) framework. It is optimized for real-time detection tasks and supports multiple deployment backends including PyTorch, TensorFlow, and ONNX.
+
+This is based on the implementation of ResNet34-SSD found [here](https://github.com/mlcommons/inference/tree/33894a19c4af6207f7cfdda75f84570f04836de5/vision/classification_and_detection).
+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/main/src/qai_hub_models/models/resnet34_ssd1200) 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.
+
+## Getting Started
+There are two ways to deploy this model on your device:
+
+### Option 1: Download Pre-Exported Models
+
+Below are pre-exported model assets ready for deployment.
+
+| Runtime | Precision | Chipset | SDK Versions | Download |
+|---|---|---|---|---|
+| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-qnn_dlc-float.zip)
+| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-tflite-float.zip)
+
+For more device-specific assets and performance metrics, visit **[ResNet34-SSD on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet34_ssd1200)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet34_ssd1200) 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 [ResNet34-SSD on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet34_ssd1200) for usage instructions.
+
+## Model Details
+
+**Model Type:** Model_use_case.object_detection
+
+**Model Stats:**
+- Model checkpoint: resnet34-ssd1200
+- Input resolution: 1x3x1200x1200
+- Number of parameters: 20.0M
+- Model size (float): 76.2 MB
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 38.083 ms | 0 - 503 MB | NPU
+| ResNet34-SSD | ONNX | float | Snapdragon® X2 Elite | 42.948 ms | 30 - 30 MB | NPU
+| ResNet34-SSD | ONNX | float | Snapdragon® X Elite | 91.439 ms | 29 - 29 MB | NPU
+| ResNet34-SSD | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 62.737 ms | 2 - 515 MB | NPU
+| ResNet34-SSD | ONNX | float | Qualcomm® QCS8550 (Proxy) | 90.435 ms | 0 - 32 MB | NPU
+| ResNet34-SSD | ONNX | float | Qualcomm® QCS9075 | 152.805 ms | 16 - 36 MB | NPU
+| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 50.221 ms | 1 - 431 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 52.144 ms | 16 - 551 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Snapdragon® X2 Elite | 61.954 ms | 17 - 17 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Snapdragon® X Elite | 129.337 ms | 17 - 17 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 84.716 ms | 16 - 607 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 481.457 ms | 16 - 385 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 129.514 ms | 17 - 20 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS9075 | 194.011 ms | 17 - 35 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 260.877 ms | 4 - 508 MB | NPU
+| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 67.232 ms | 16 - 394 MB | NPU
+| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 74.9 ms | 0 - 564 MB | NPU
+| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 108.177 ms | 0 - 547 MB | NPU
+| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 513.551 ms | 0 - 377 MB | NPU
+| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 143.313 ms | 0 - 4 MB | NPU
+| ResNet34-SSD | TFLITE | float | Qualcomm® QCS9075 | 199.657 ms | 0 - 64 MB | NPU
+| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 232.566 ms | 1 - 616 MB | NPU
+| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 86.771 ms | 19 - 421 MB | NPU
+
+## License
+* The license for the original implementation of ResNet34-SSD can be found
+  [here](https://github.com/mlcommons/inference/blob/33894a19c4af6207f7cfdda75f84570f04836de5/LICENSE.md).
+
+## References
+* [Source Model Implementation](https://github.com/mlcommons/inference/tree/33894a19c4af6207f7cfdda75f84570f04836de5/vision/classification_and_detection)
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).

release_assets.json

@@ -0,0 +1,29 @@
+{
+    "version": "0.52.0",
+    "precisions": {
+        "float": {
+            "universal_assets": {
+                "tflite": {
+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327",
+                        "litert": "1.4.2"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-tflite-float.zip"
+                },
+                "qnn_dlc": {
+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-qnn_dlc-float.zip"
+                },
+                "onnx": {
+                    "tool_versions": {
+                        "qairt": "2.42.0.251225135753_193295",
+                        "onnx_runtime": "1.24.3"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet34_ssd1200/releases/v0.52.0/resnet34_ssd1200-onnx-float.zip"
+                }
+            }
+        }
+    }
+}