v0.66.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.66.0 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/relja/netvlad/blob/master/LICENCE.

README.md

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+---
+library_name: pytorch
+license: mit
+tags:
+- backbone
+- android
+pipeline_tag: image-classification
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/web-assets/model_demo.png)
+
+# NetVLAD: Optimized for Qualcomm Devices
+
+NetVLAD is a CNN-based visual place recognition model that aggregates dense local descriptors into a compact VLAD descriptor using a differentiable soft assignment to learned cluster centroids. Built on a VGG-16 backbone, it produces a 32,768-d L2-normalized descriptor suitable for nearest-neighbour place retrieval across images taken under varying conditions.
+
+This is based on the implementation of NetVLAD found [here](https://github.com/Nanne/pytorch-NetVlad).
+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/v0.66.0/src/qai_hub_models/models/netvlad) 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 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-onnx-float.zip)
+| ONNX | w8a8_mixed_int16 | Universal | QAIRT 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-onnx-w8a8_mixed_int16.zip)
+| QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-qnn_dlc-float.zip)
+| QNN_DLC | w8a8_mixed_int16 | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-qnn_dlc-w8a8_mixed_int16.zip)
+| TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-tflite-float.zip)
+
+For more device-specific assets and performance metrics, visit **[NetVLAD on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/netvlad)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.66.0/src/qai_hub_models/models/netvlad) 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 [NetVLAD on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.66.0/src/qai_hub_models/models/netvlad) for usage instructions.
+
+## Model Details
+
+**Model Type:** Model_use_case.image_classification
+
+**Model Stats:**
+
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| NetVLAD | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.288 ms | 0 - 141 MB | NPU
+| NetVLAD | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 11.288 ms | 0 - 141 MB | NPU
+| NetVLAD | ONNX | w8a8_mixed_int16 | Snapdragon® 8 Elite For Galaxy Mobile | 3.211 ms | 0 - 144 MB | NPU
+| NetVLAD | ONNX | w8a8_mixed_int16 | Qualcomm® Dragonwing™ Q-8750 | 3.211 ms | 0 - 144 MB | NPU
+| NetVLAD | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 12.49 ms | 4 - 143 MB | NPU
+| NetVLAD | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 12.49 ms | 4 - 143 MB | NPU
+| NetVLAD | QNN_DLC | w8a8_mixed_int16 | Snapdragon® 8 Elite For Galaxy Mobile | 3.16 ms | 0 - 142 MB | NPU
+| NetVLAD | QNN_DLC | w8a8_mixed_int16 | Qualcomm® Dragonwing™ Q-8750 | 3.16 ms | 0 - 142 MB | NPU
+| NetVLAD | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.272 ms | 0 - 145 MB | NPU
+| NetVLAD | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 11.272 ms | 0 - 145 MB | NPU
+
+## License
+* The license for the original implementation of NetVLAD can be found
+  [here](https://github.com/relja/netvlad/blob/master/LICENCE).
+
+## References
+* [NetVLAD: CNN architecture for weakly supervised place recognition](https://arxiv.org/abs/1511.07247)
+* [Source Model Implementation](https://github.com/Nanne/pytorch-NetVlad)
+
+## 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

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+{
+    "version": "0.66.0",
+    "precisions": {
+        "float": {
+            "universal_assets": {
+                "onnx": {
+                    "tool_versions": {
+                        "qairt": "2.50.0.260828221209",
+                        "onnx_runtime": "1.30.0"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-onnx-float.zip"
+                },
+                "qnn_dlc": {
+                    "tool_versions": {
+                        "qairt": "2.50.0.260828221209"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-qnn_dlc-float.zip"
+                },
+                "tflite": {
+                    "tool_versions": {
+                        "qairt": "2.50.0.260828221209",
+                        "litert": "1.4.2"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-tflite-float.zip"
+                }
+            }
+        },
+        "w8a8_mixed_int16": {
+            "universal_assets": {
+                "onnx": {
+                    "tool_versions": {
+                        "qairt": "2.50.0.260828221209",
+                        "onnx_runtime": "1.30.0"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-onnx-w8a8_mixed_int16.zip"
+                },
+                "qnn_dlc": {
+                    "tool_versions": {
+                        "qairt": "2.50.0.260828221209"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/netvlad/releases/v0.66.0/netvlad-qnn_dlc-w8a8_mixed_int16.zip"
+                }
+            }
+        }
+    }
+}