Image-to-Image
PyTorch
android
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See https://github.com/qualcomm/ai-hub-models/releases/v0.57.3 for changelog.

Files changed (2) hide show
  1. README.md +8 -8
  2. release_assets.json +6 -6
README.md CHANGED
@@ -14,7 +14,7 @@ pipeline_tag: image-to-image
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  ESRGAN is a machine learning model that upscales an image with minimal loss in quality.
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  This is based on the implementation of ESRGAN found [here](https://github.com/xinntao/ESRGAN/).
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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/v0.57.2/src/qai_hub_models/models/esrgan) 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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@@ -27,25 +27,25 @@ 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 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.2/esrgan-onnx-float.zip)
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- | ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.2/esrgan-onnx-w8a16.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/esrgan/releases/v0.57.2/esrgan-qnn_dlc-float.zip)
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- | QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.2/esrgan-qnn_dlc-w8a16.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/esrgan/releases/v0.57.2/esrgan-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[ESRGAN on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/esrgan)**.
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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/v0.57.2/src/qai_hub_models/models/esrgan) 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 [ESRGAN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.2/src/qai_hub_models/models/esrgan) for usage instructions.
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  ## Model Details
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  ESRGAN is a machine learning model that upscales an image with minimal loss in quality.
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  This is based on the implementation of ESRGAN found [here](https://github.com/xinntao/ESRGAN/).
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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/v0.57.3/src/qai_hub_models/models/esrgan) 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 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.3/esrgan-onnx-float.zip)
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+ | ONNX | w8a16 | Universal | QAIRT 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.3/esrgan-onnx-w8a16.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/esrgan/releases/v0.57.3/esrgan-qnn_dlc-float.zip)
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+ | QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.3/esrgan-qnn_dlc-w8a16.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/esrgan/releases/v0.57.3/esrgan-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[ESRGAN on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/esrgan)**.
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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/v0.57.3/src/qai_hub_models/models/esrgan) Python library to compile and export the model with your own:
42
  - Custom weights (e.g., fine-tuned checkpoints)
43
  - Custom input shapes
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  - Target device and runtime configurations
45
 
46
  This option is ideal if you need to customize the model beyond the default configuration provided here.
47
 
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+ See our repository for [ESRGAN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/esrgan) 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": "0.57.2",
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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/esrgan/releases/v0.57.2/esrgan-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/esrgan/releases/v0.57.2/esrgan-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": "1.25.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.2/esrgan-onnx-float.zip"
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  }
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  }
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  },
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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/esrgan/releases/v0.57.2/esrgan-qnn_dlc-w8a16.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": "1.25.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.2/esrgan-onnx-w8a16.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.57.3",
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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/esrgan/releases/v0.57.3/esrgan-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/esrgan/releases/v0.57.3/esrgan-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": "1.25.0"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.3/esrgan-onnx-float.zip"
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  }
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  }
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  },
 
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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/esrgan/releases/v0.57.3/esrgan-qnn_dlc-w8a16.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": "1.25.0"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/esrgan/releases/v0.57.3/esrgan-onnx-w8a16.zip"
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  }
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  }
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  }