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See https://github.com/qualcomm/ai-hub-models/releases/v0.57.1 for changelog.

Files changed (2) hide show
  1. README.md +9 -9
  2. release_assets.json +7 -7
README.md CHANGED
@@ -16,7 +16,7 @@ pipeline_tag: image-classification
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  EfficientNet-Lite4 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
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  This is based on the implementation of EfficientNet-Lite4 found [here](https://github.com/RangiLyu/EfficientNet-Lite).
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- 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/v0.57.0/src/qai_hub_models/models/efficientnet_lite4) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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  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.
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@@ -29,26 +29,26 @@ Below are pre-exported model assets ready for deployment.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
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  |---|---|---|---|---|
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- | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.26.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-onnx-float.zip)
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- | ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.26.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-onnx-w8a8.zip)
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- | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-qnn_dlc-float.zip)
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- | QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-qnn_dlc-w8a8.zip)
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- | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-tflite-float.zip)
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- | TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-tflite-w8a8.zip)
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  For more device-specific assets and performance metrics, visit **[EfficientNet-Lite4 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/efficientnet_lite4)**.
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  ### Option 2: Export with Custom Configurations
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- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.0/src/qai_hub_models/models/efficientnet_lite4) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
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  - Custom input shapes
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  - Target device and runtime configurations
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  This option is ideal if you need to customize the model beyond the default configuration provided here.
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- See our repository for [EfficientNet-Lite4 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.0/src/qai_hub_models/models/efficientnet_lite4) for usage instructions.
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  ## Model Details
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  EfficientNet-Lite4 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
17
 
18
  This is based on the implementation of EfficientNet-Lite4 found [here](https://github.com/RangiLyu/EfficientNet-Lite).
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+ 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/v0.57.1/src/qai_hub_models/models/efficientnet_lite4) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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  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.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
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  |---|---|---|---|---|
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+ | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.26.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-onnx-float.zip)
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+ | ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.26.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-onnx-w8a8.zip)
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+ | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-qnn_dlc-float.zip)
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+ | QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-qnn_dlc-w8a8.zip)
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+ | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-tflite-float.zip)
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+ | TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-tflite-w8a8.zip)
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  For more device-specific assets and performance metrics, visit **[EfficientNet-Lite4 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/efficientnet_lite4)**.
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  ### Option 2: Export with Custom Configurations
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+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.1/src/qai_hub_models/models/efficientnet_lite4) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
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  - Custom input shapes
47
  - Target device and runtime configurations
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  This option is ideal if you need to customize the model beyond the default configuration provided here.
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+ See our repository for [EfficientNet-Lite4 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.1/src/qai_hub_models/models/efficientnet_lite4) for usage instructions.
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  ## Model Details
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release_assets.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "version": "0.57.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
@@ -8,20 +8,20 @@
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  "qairt": "2.45.0.260326154327",
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  "litert": "1.4.4"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-tflite-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-qnn_dlc-float.zip"
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  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.26.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-onnx-float.zip"
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  }
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  }
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  },
@@ -32,20 +32,20 @@
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  "qairt": "2.45.0.260326154327",
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  "litert": "1.4.4"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-tflite-w8a8.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-qnn_dlc-w8a8.zip"
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  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.26.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.0/efficientnet_lite4-onnx-w8a8.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.57.1",
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  "precisions": {
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  "float": {
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  "universal_assets": {
 
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  "qairt": "2.45.0.260326154327",
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  "litert": "1.4.4"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-tflite-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-qnn_dlc-float.zip"
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  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.26.0"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-onnx-float.zip"
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  }
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  }
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  },
 
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  "qairt": "2.45.0.260326154327",
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  "litert": "1.4.4"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-tflite-w8a8.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-qnn_dlc-w8a8.zip"
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  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.26.0"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/efficientnet_lite4/releases/v0.57.1/efficientnet_lite4-onnx-w8a8.zip"
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  }
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  }
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  }