v0.48.0
Browse filesSee https://github.com/qualcomm/ai-hub-models/releases/v0.48.0 for changelog.
README.md
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FCN_ResNet50 is a machine learning model that can segment images from the COCO dataset. It uses ResNet50 as a backbone.
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This is based on the implementation of FCN-ResNet50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/fcn.py).
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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/
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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 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.
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For more device-specific assets and performance metrics, visit **[FCN-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/fcn_resnet50)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/
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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 [FCN-ResNet50 on GitHub](https://github.com/
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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| FCN-ResNet50 | ONNX | float | Snapdragon®
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| FCN-ResNet50 | ONNX | float | Snapdragon®
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| FCN-ResNet50 | ONNX | float |
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| FCN-ResNet50 | ONNX | float | Qualcomm®
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| FCN-ResNet50 | ONNX | float |
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| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite
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| FCN-ResNet50 | ONNX | float | Snapdragon®
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon®
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon®
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| FCN-ResNet50 | ONNX | w8a8 |
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm®
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm®
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm®
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| FCN-ResNet50 | ONNX | w8a8 |
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon®
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon®
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | float |
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | float |
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | w8a8 |
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm®
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| FCN-ResNet50 | QNN_DLC | w8a8 |
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon®
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon®
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 33.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 272.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 44.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 72.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 77.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) |
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 272.
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 77.
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 20.
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| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 94.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 38.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8775P |
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 15.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCM6690 |
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 23.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA7255P | 38.
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8295P | 21.
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| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.
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| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 26.
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| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.
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## License
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* The license for the original implementation of FCN-ResNet50 can be found
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FCN_ResNet50 is a machine learning model that can segment images from the COCO dataset. It uses ResNet50 as a backbone.
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This is based on the implementation of FCN-ResNet50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/fcn.py).
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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/main/qai_hub_models/models/fcn_resnet50) 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 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-onnx-float.zip)
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| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-onnx-w8a8.zip)
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| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-qnn_dlc-float.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-qnn_dlc-w8a8.zip)
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| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.48.0/fcn_resnet50-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[FCN-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/fcn_resnet50)**.
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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/main/qai_hub_models/models/fcn_resnet50) 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 [FCN-ResNet50 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/fcn_resnet50) for usage instructions.
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| FCN-ResNet50 | ONNX | float | Snapdragon® X2 Elite | 23.447 ms | 63 - 63 MB | NPU
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| FCN-ResNet50 | ONNX | float | Snapdragon® X Elite | 43.269 ms | 62 - 62 MB | NPU
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| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 32.581 ms | 0 - 378 MB | NPU
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| FCN-ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 43.247 ms | 3 - 6 MB | NPU
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| FCN-ResNet50 | ONNX | float | Qualcomm® QCS9075 | 75.296 ms | 3 - 9 MB | NPU
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| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.716 ms | 1 - 309 MB | NPU
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| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 22.542 ms | 4 - 325 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X2 Elite | 7.109 ms | 33 - 33 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X Elite | 13.851 ms | 32 - 32 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 10.035 ms | 0 - 279 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS6490 | 912.318 ms | 66 - 110 MB | CPU
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 13.534 ms | 1 - 4 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS9075 | 15.135 ms | 1 - 4 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCM6690 | 835.668 ms | 66 - 74 MB | CPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.613 ms | 1 - 203 MB | NPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 703.386 ms | 69 - 78 MB | CPU
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| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.057 ms | 0 - 248 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X2 Elite | 22.759 ms | 3 - 3 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 44.235 ms | 3 - 3 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 33.595 ms | 3 - 387 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 272.3 ms | 1 - 304 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 44.873 ms | 3 - 33 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 72.455 ms | 1 - 305 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 78.99 ms | 5 - 10 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 84.255 ms | 3 - 280 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 272.3 ms | 1 - 304 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 77.735 ms | 0 - 218 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.506 ms | 0 - 319 MB | NPU
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| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 21.795 ms | 2 - 330 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 7.88 ms | 1 - 1 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X Elite | 15.169 ms | 1 - 1 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.475 ms | 1 - 268 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 93.25 ms | 3 - 5 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 39.566 ms | 1 - 205 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.992 ms | 1 - 3 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 64.676 ms | 1 - 215 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 16.846 ms | 1 - 3 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 416.057 ms | 1 - 347 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 24.324 ms | 1 - 266 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 39.566 ms | 1 - 205 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 22.131 ms | 1 - 209 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.868 ms | 0 - 200 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 25.738 ms | 1 - 280 MB | NPU
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| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.554 ms | 1 - 245 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 33.053 ms | 0 - 427 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 272.32 ms | 0 - 332 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 44.582 ms | 0 - 3 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 72.525 ms | 0 - 331 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 77.893 ms | 0 - 71 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 84.829 ms | 0 - 318 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 272.32 ms | 0 - 332 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 77.792 ms | 0 - 246 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.206 ms | 0 - 343 MB | NPU
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| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 20.506 ms | 0 - 351 MB | NPU
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| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.056 ms | 0 - 270 MB | NPU
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 94.88 ms | 0 - 39 MB | NPU
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 38.248 ms | 0 - 206 MB | NPU
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.338 ms | 0 - 2 MB | NPU
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| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8775P | 62.896 ms | 0 - 205 MB | NPU
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+
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 15.34 ms | 0 - 35 MB | NPU
|
| 126 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCM6690 | 426.83 ms | 0 - 353 MB | NPU
|
| 127 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 23.255 ms | 0 - 267 MB | NPU
|
| 128 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA7255P | 38.248 ms | 0 - 206 MB | NPU
|
| 129 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8295P | 21.364 ms | 0 - 209 MB | NPU
|
| 130 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.551 ms | 0 - 197 MB | NPU
|
| 131 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 26.585 ms | 0 - 282 MB | NPU
|
| 132 |
+
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.164 ms | 0 - 246 MB | NPU
|
| 133 |
|
| 134 |
## License
|
| 135 |
* The license for the original implementation of FCN-ResNet50 can be found
|