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--- |
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library_name: pytorch |
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license: other |
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tags: |
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- bu_auto |
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- android |
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pipeline_tag: image-classification |
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--- |
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# ConvNext-Tiny: Optimized for Qualcomm Devices |
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ConvNextTiny 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 ConvNext-Tiny found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.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/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_tiny) 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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## Getting Started |
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There are two ways to deploy this model on your device: |
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### Option 1: Download Pre-Exported Models |
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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 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_tiny/releases/v0.46.0/convnext_tiny-onnx-float.zip) |
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| 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_tiny/releases/v0.46.0/convnext_tiny-onnx-w8a16.zip) |
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_tiny/releases/v0.46.0/convnext_tiny-qnn_dlc-float.zip) |
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| QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/convnext_tiny/releases/v0.46.0/convnext_tiny-qnn_dlc-w8a16.zip) |
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| 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_tiny/releases/v0.46.0/convnext_tiny-tflite-float.zip) |
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For more device-specific assets and performance metrics, visit **[ConvNext-Tiny on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/convnext_tiny)**. |
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### Option 2: Export with Custom Configurations |
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Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_tiny) 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 [ConvNext-Tiny on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/convnext_tiny) for usage instructions. |
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## Model Details |
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**Model Type:** Model_use_case.image_classification |
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**Model Stats:** |
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- Model checkpoint: Imagenet |
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- Input resolution: 224x224 |
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- Number of parameters: 28.6M |
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- Model size (float): 109 MB |
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- Model size (w8a16): 28.9 MB |
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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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| ConvNext-Tiny | ONNX | float | Snapdragon® X Elite | 2.871 ms | 57 - 57 MB | NPU |
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| ConvNext-Tiny | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 2.254 ms | 0 - 227 MB | NPU |
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| ConvNext-Tiny | ONNX | float | Qualcomm® QCS8550 (Proxy) | 3.041 ms | 0 - 66 MB | NPU |
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| ConvNext-Tiny | ONNX | float | Qualcomm® QCS9075 | 4.152 ms | 1 - 4 MB | NPU |
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| ConvNext-Tiny | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.798 ms | 0 - 181 MB | NPU |
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| ConvNext-Tiny | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.465 ms | 1 - 182 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Snapdragon® X Elite | 66.853 ms | 62 - 62 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 88.472 ms | 45 - 194 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Qualcomm® QCS6490 | 397.835 ms | 50 - 69 MB | CPU |
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| ConvNext-Tiny | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 99.954 ms | 42 - 45 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Qualcomm® QCS9075 | 105.638 ms | 48 - 50 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Qualcomm® QCM6690 | 244.731 ms | 59 - 72 MB | CPU |
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| ConvNext-Tiny | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 76.665 ms | 45 - 162 MB | NPU |
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| ConvNext-Tiny | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 231.806 ms | 57 - 71 MB | CPU |
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| ConvNext-Tiny | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 70.719 ms | 42 - 160 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Snapdragon® X Elite | 3.932 ms | 1 - 1 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 2.665 ms | 1 - 170 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 15.324 ms | 1 - 124 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 3.68 ms | 1 - 2 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® SA8775P | 5.008 ms | 1 - 126 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® QCS9075 | 4.856 ms | 1 - 3 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 9.628 ms | 0 - 168 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® SA7255P | 15.324 ms | 1 - 124 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Qualcomm® SA8295P | 8.903 ms | 1 - 125 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.028 ms | 0 - 126 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.614 ms | 1 - 127 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Snapdragon® X Elite | 3.411 ms | 0 - 0 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 2.17 ms | 0 - 121 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 9.087 ms | 0 - 2 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 6.814 ms | 0 - 96 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 3.135 ms | 0 - 2 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® SA8775P | 3.46 ms | 0 - 97 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 3.331 ms | 0 - 2 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 23.577 ms | 0 - 250 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 4.17 ms | 0 - 124 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® SA7255P | 6.814 ms | 0 - 96 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Qualcomm® SA8295P | 4.656 ms | 0 - 97 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.591 ms | 0 - 101 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 3.428 ms | 0 - 108 MB | NPU |
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| ConvNext-Tiny | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 1.286 ms | 0 - 100 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.204 ms | 0 - 169 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 14.244 ms | 0 - 122 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.913 ms | 0 - 2 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® SA8775P | 4.271 ms | 0 - 123 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® QCS9075 | 4.082 ms | 0 - 59 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 8.877 ms | 0 - 161 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® SA7255P | 14.244 ms | 0 - 122 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Qualcomm® SA8295P | 7.835 ms | 0 - 119 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.643 ms | 0 - 127 MB | NPU |
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| ConvNext-Tiny | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.333 ms | 0 - 121 MB | NPU |
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## License |
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* The license for the original implementation of ConvNext-Tiny can be found |
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[here](https://github.com/pytorch/vision/blob/main/LICENSE). |
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## References |
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* [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) |
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* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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