ConvNext-Base / README.md

v0.46.0

595b66e verified 8 days ago

7.85 kB

	---
	library_name: pytorch
	license: other
	tags:
	- bu_auto
	- android
	pipeline_tag: image-classification

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/web-assets/model_demo.png)

	# ConvNext-Base: Optimized for Qualcomm Devices

	ConvNextBase 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.

	This is based on the implementation of ConvNext-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py).
	This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) 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 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-onnx-float.zip)
	\| ONNX \| w8a16 \| Universal \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-onnx-w8a16.zip)
	\| QNN_DLC \| float \| Universal \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-qnn_dlc-float.zip)
	\| QNN_DLC \| w8a16 \| Universal \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-qnn_dlc-w8a16.zip)
	\| TFLITE \| float \| Universal \| QAIRT 2.42, TFLite 2.17.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-tflite-float.zip)

	For more device-specific assets and performance metrics, visit [ConvNext-Base on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/convnext_base).


	### Option 2: Export with Custom Configurations

	Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) 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 [ConvNext-Base on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) for usage instructions.

	## Model Details

	Model Type: Model_use_case.image_classification

	Model Stats:
	- Model checkpoint: Imagenet
	- Input resolution: 224x224
	- Number of parameters: 88.6M
	- Model size (float): 338 MB
	- Model size (w8a16): 88.7 MB

	## Performance Summary
	\| Model \| Runtime \| Precision \| Chipset \| Inference Time (ms) \| Peak Memory Range (MB) \| Primary Compute Unit
	\|---\|---\|---\|---\|---\|---\|---
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® X Elite \| 7.488 ms \| 176 - 176 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 5.436 ms \| 0 - 394 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 7.317 ms \| 0 - 638 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Qualcomm® QCS9075 \| 11.598 ms \| 0 - 4 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.246 ms \| 0 - 329 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.352 ms \| 0 - 332 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® X Elite \| 219.265 ms \| 137 - 137 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCS6490 \| 1158.253 ms \| 41 - 86 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCS9075 \| 317.703 ms \| 93 - 96 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCM6690 \| 737.008 ms \| 34 - 46 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 268.327 ms \| 78 - 229 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 7 Gen 4 Mobile \| 691.251 ms \| 35 - 49 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 237.073 ms \| 89 - 240 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® X Elite \| 8.589 ms \| 1 - 1 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Gen 3 Mobile \| 6.113 ms \| 0 - 350 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8275 (Proxy) \| 42.453 ms \| 1 - 279 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8550 (Proxy) \| 8.213 ms \| 0 - 33 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS9075 \| 12.381 ms \| 1 - 3 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8450 (Proxy) \| 20.603 ms \| 0 - 337 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.689 ms \| 1 - 281 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.534 ms \| 1 - 283 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® X Elite \| 6.26 ms \| 0 - 0 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Gen 3 Mobile \| 4.106 ms \| 0 - 248 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS6490 \| 23.818 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8275 (Proxy) \| 14.472 ms \| 0 - 199 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8550 (Proxy) \| 5.888 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS9075 \| 6.122 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCM6690 \| 71.461 ms \| 0 - 395 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8450 (Proxy) \| 9.182 ms \| 0 - 246 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 3.31 ms \| 0 - 190 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 7 Gen 4 Mobile \| 7.715 ms \| 0 - 247 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 2.559 ms \| 0 - 201 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Gen 3 Mobile \| 5.533 ms \| 0 - 345 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8275 (Proxy) \| 41.241 ms \| 0 - 274 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8550 (Proxy) \| 7.334 ms \| 0 - 3 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS9075 \| 11.149 ms \| 0 - 177 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8450 (Proxy) \| 19.73 ms \| 0 - 330 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.167 ms \| 0 - 277 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.174 ms \| 0 - 279 MB \| NPU

	## License
	* The license for the original implementation of ConvNext-Base can be found
	[here](https://github.com/pytorch/vision/blob/main/LICENSE).

	## References
	* [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
	* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py)

	## 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).

	---
	library_name: pytorch
	license: other
	tags:
	- bu_auto
	- android
	pipeline_tag: image-classification

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/web-assets/model_demo.png)

	# ConvNext-Base: Optimized for Qualcomm Devices

	ConvNextBase 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.

	This is based on the implementation of ConvNext-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py).
	This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) 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 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-onnx-float.zip)
	\| ONNX \| w8a16 \| Universal \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-onnx-w8a16.zip)
	\| QNN_DLC \| float \| Universal \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-qnn_dlc-float.zip)
	\| QNN_DLC \| w8a16 \| Universal \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-qnn_dlc-w8a16.zip)
	\| TFLITE \| float \| Universal \| QAIRT 2.42, TFLite 2.17.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_base/releases/v0.46.0/convnext_base-tflite-float.zip)

	For more device-specific assets and performance metrics, visit [ConvNext-Base on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/convnext_base).


	### Option 2: Export with Custom Configurations

	Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) 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 [ConvNext-Base on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_base) for usage instructions.

	## Model Details

	Model Type: Model_use_case.image_classification

	Model Stats:
	- Model checkpoint: Imagenet
	- Input resolution: 224x224
	- Number of parameters: 88.6M
	- Model size (float): 338 MB
	- Model size (w8a16): 88.7 MB

	## Performance Summary
	\| Model \| Runtime \| Precision \| Chipset \| Inference Time (ms) \| Peak Memory Range (MB) \| Primary Compute Unit
	\|---\|---\|---\|---\|---\|---\|---
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® X Elite \| 7.488 ms \| 176 - 176 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 5.436 ms \| 0 - 394 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 7.317 ms \| 0 - 638 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Qualcomm® QCS9075 \| 11.598 ms \| 0 - 4 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.246 ms \| 0 - 329 MB \| NPU
	\| ConvNext-Base \| ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.352 ms \| 0 - 332 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® X Elite \| 219.265 ms \| 137 - 137 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCS6490 \| 1158.253 ms \| 41 - 86 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCS9075 \| 317.703 ms \| 93 - 96 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Qualcomm® QCM6690 \| 737.008 ms \| 34 - 46 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 268.327 ms \| 78 - 229 MB \| NPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 7 Gen 4 Mobile \| 691.251 ms \| 35 - 49 MB \| CPU
	\| ConvNext-Base \| ONNX \| w8a16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 237.073 ms \| 89 - 240 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® X Elite \| 8.589 ms \| 1 - 1 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Gen 3 Mobile \| 6.113 ms \| 0 - 350 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8275 (Proxy) \| 42.453 ms \| 1 - 279 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8550 (Proxy) \| 8.213 ms \| 0 - 33 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS9075 \| 12.381 ms \| 1 - 3 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Qualcomm® QCS8450 (Proxy) \| 20.603 ms \| 0 - 337 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.689 ms \| 1 - 281 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.534 ms \| 1 - 283 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® X Elite \| 6.26 ms \| 0 - 0 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Gen 3 Mobile \| 4.106 ms \| 0 - 248 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS6490 \| 23.818 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8275 (Proxy) \| 14.472 ms \| 0 - 199 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8550 (Proxy) \| 5.888 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS9075 \| 6.122 ms \| 0 - 2 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCM6690 \| 71.461 ms \| 0 - 395 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Qualcomm® QCS8450 (Proxy) \| 9.182 ms \| 0 - 246 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Elite For Galaxy Mobile \| 3.31 ms \| 0 - 190 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 7 Gen 4 Mobile \| 7.715 ms \| 0 - 247 MB \| NPU
	\| ConvNext-Base \| QNN_DLC \| w8a16 \| Snapdragon® 8 Elite Gen 5 Mobile \| 2.559 ms \| 0 - 201 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Gen 3 Mobile \| 5.533 ms \| 0 - 345 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8275 (Proxy) \| 41.241 ms \| 0 - 274 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8550 (Proxy) \| 7.334 ms \| 0 - 3 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS9075 \| 11.149 ms \| 0 - 177 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Qualcomm® QCS8450 (Proxy) \| 19.73 ms \| 0 - 330 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 4.167 ms \| 0 - 277 MB \| NPU
	\| ConvNext-Base \| TFLITE \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 3.174 ms \| 0 - 279 MB \| NPU

	## License
	* The license for the original implementation of ConvNext-Base can be found
	[here](https://github.com/pytorch/vision/blob/main/LICENSE).

	## References
	* [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
	* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py)

	## 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).