v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- README.md +113 -262
- RegNet_float.dlc +0 -3
- RegNet_float.onnx.zip +0 -3
- RegNet_float.tflite +0 -3
- RegNet_w8a8.dlc +0 -3
- RegNet_w8a8.onnx.zip +0 -3
- RegNet_w8a8.tflite +0 -3
- tool-versions.yaml +0 -4
README.md
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# RegNet: Optimized for
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## Imagenet classifier and general purpose backbone
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RegNet 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
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| RegNet |
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weights and runs this model on a sample input.
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```bash
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python -m qai_hub_models.models.regnet.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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inference, and post processing.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.regnet.demo
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```
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### Run model on a cloud-hosted device
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Downloads compiled assets that can be deployed on-device for Android.
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* Accuracy check between PyTorch and on-device outputs.
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```bash
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python -m qai_hub_models.models.regnet.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/regnet/qai_hub_models/models/RegNet/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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To compile a PyTorch model for on-device deployment, we first trace the model
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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```python
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import torch
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import qai_hub as hub
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from qai_hub_models.models.regnet import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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# Trace model
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input_shape = torch_model.get_input_spec()
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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model=pt_model,
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device=device,
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input_specs=torch_model.get_input_spec(),
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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Step 2: **Performance profiling on cloud-hosted device**
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After compiling models from step 1. Models can be profiled model on-device using the
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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```python
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profile_job = hub.submit_profile_job(
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model=target_model,
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device=device,
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)
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```
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Step 3: **Verify on-device accuracy**
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To verify the accuracy of the model on-device, you can run on-device inference
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on sample input data on the same cloud hosted device.
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```python
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input_data = torch_model.sample_inputs()
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inference_job = hub.submit_inference_job(
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model=target_model,
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device=device,
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inputs=input_data,
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)
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on_device_output = inference_job.download_output_data()
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```
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With the output of the model, you can compute like PSNR, relative errors or
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spot check the output with expected output.
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**Note**: This on-device profiling and inference requires access to Qualcomm®
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Run demo on a cloud-hosted device
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You can also run the demo on-device.
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```bash
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python -m qai_hub_models.models.regnet.demo --eval-mode on-device
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```
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.regnet.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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- TensorFlow Lite (`.tflite` export): [This
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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guide to deploy the .tflite model in an Android application.
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- QNN (`.so` export ): This [sample
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on RegNet's performance across various devices [here](https://aihub.qualcomm.com/models/regnet).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of RegNet 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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* [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)
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* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/regnet.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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# RegNet: Optimized for Qualcomm Devices
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RegNet 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 RegNet found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/regnet.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/regnet) 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/regnet/releases/v0.46.1/regnet-onnx-float.zip)
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| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/regnet/releases/v0.46.1/regnet-onnx-w8a8.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/regnet/releases/v0.46.1/regnet-qnn_dlc-float.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/regnet/releases/v0.46.1/regnet-qnn_dlc-w8a8.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/regnet/releases/v0.46.1/regnet-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/regnet/releases/v0.46.1/regnet-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[RegNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/regnet)**.
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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/regnet) 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 [RegNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/regnet) 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: 15.3M
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- Model size (float): 58.3 MB
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- Model size (w8a8): 15.4 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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| RegNet | ONNX | float | Snapdragon® X Elite | 1.933 ms | 39 - 39 MB | NPU
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| RegNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1.509 ms | 0 - 186 MB | NPU
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| RegNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2.038 ms | 0 - 44 MB | NPU
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| RegNet | ONNX | float | Qualcomm® QCS9075 | 3.075 ms | 0 - 4 MB | NPU
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| RegNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.239 ms | 0 - 140 MB | NPU
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| RegNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.021 ms | 0 - 140 MB | NPU
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| RegNet | ONNX | w8a8 | Snapdragon® X Elite | 1.137 ms | 20 - 20 MB | NPU
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| RegNet | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.87 ms | 0 - 197 MB | NPU
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| RegNet | ONNX | w8a8 | Qualcomm® QCS6490 | 27.939 ms | 7 - 18 MB | CPU
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| RegNet | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 1.184 ms | 0 - 107 MB | NPU
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| RegNet | ONNX | w8a8 | Qualcomm® QCS9075 | 1.324 ms | 0 - 3 MB | NPU
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| RegNet | ONNX | w8a8 | Qualcomm® QCM6690 | 18.049 ms | 8 - 17 MB | CPU
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| RegNet | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.722 ms | 0 - 152 MB | NPU
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| RegNet | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 13.384 ms | 9 - 18 MB | CPU
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| RegNet | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.674 ms | 0 - 153 MB | NPU
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| RegNet | QNN_DLC | float | Snapdragon® X Elite | 2.287 ms | 1 - 1 MB | NPU
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| RegNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.413 ms | 0 - 128 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 9.987 ms | 1 - 76 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.051 ms | 1 - 135 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® SA8775P | 3.263 ms | 0 - 79 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® QCS9075 | 3.058 ms | 1 - 3 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 3.482 ms | 0 - 115 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® SA7255P | 9.987 ms | 1 - 76 MB | NPU
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| RegNet | QNN_DLC | float | Qualcomm® SA8295P | 3.5 ms | 1 - 66 MB | NPU
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| RegNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.106 ms | 0 - 80 MB | NPU
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| RegNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.889 ms | 1 - 81 MB | NPU
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| RegNet | QNN_DLC | w8a8 | Snapdragon® X Elite | 1.071 ms | 0 - 0 MB | NPU
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| RegNet | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.638 ms | 0 - 109 MB | NPU
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| RegNet | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 2.712 ms | 0 - 2 MB | NPU
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| RegNet | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 2.3 ms | 0 - 76 MB | NPU
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| 96 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.886 ms | 0 - 2 MB | NPU
|
| 97 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® SA8775P | 1.199 ms | 0 - 78 MB | NPU
|
| 98 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 1.068 ms | 0 - 2 MB | NPU
|
| 99 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 7.075 ms | 0 - 196 MB | NPU
|
| 100 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.295 ms | 0 - 110 MB | NPU
|
| 101 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® SA7255P | 2.3 ms | 0 - 76 MB | NPU
|
| 102 |
+
| RegNet | QNN_DLC | w8a8 | Qualcomm® SA8295P | 1.606 ms | 0 - 76 MB | NPU
|
| 103 |
+
| RegNet | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.493 ms | 0 - 76 MB | NPU
|
| 104 |
+
| RegNet | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.153 ms | 0 - 76 MB | NPU
|
| 105 |
+
| RegNet | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.428 ms | 0 - 80 MB | NPU
|
| 106 |
+
| RegNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1.417 ms | 0 - 160 MB | NPU
|
| 107 |
+
| RegNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 9.908 ms | 0 - 104 MB | NPU
|
| 108 |
+
| RegNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.031 ms | 0 - 3 MB | NPU
|
| 109 |
+
| RegNet | TFLITE | float | Qualcomm® SA8775P | 13.985 ms | 0 - 104 MB | NPU
|
| 110 |
+
| RegNet | TFLITE | float | Qualcomm® QCS9075 | 3.046 ms | 0 - 42 MB | NPU
|
| 111 |
+
| RegNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 3.455 ms | 0 - 144 MB | NPU
|
| 112 |
+
| RegNet | TFLITE | float | Qualcomm® SA7255P | 9.908 ms | 0 - 104 MB | NPU
|
| 113 |
+
| RegNet | TFLITE | float | Qualcomm® SA8295P | 3.52 ms | 0 - 83 MB | NPU
|
| 114 |
+
| RegNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.095 ms | 0 - 106 MB | NPU
|
| 115 |
+
| RegNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.887 ms | 0 - 106 MB | NPU
|
| 116 |
+
| RegNet | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.536 ms | 0 - 119 MB | NPU
|
| 117 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCS6490 | 2.321 ms | 0 - 22 MB | NPU
|
| 118 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.992 ms | 0 - 76 MB | NPU
|
| 119 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.722 ms | 0 - 2 MB | NPU
|
| 120 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® SA8775P | 1.047 ms | 0 - 77 MB | NPU
|
| 121 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.888 ms | 0 - 22 MB | NPU
|
| 122 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCM6690 | 6.625 ms | 0 - 190 MB | NPU
|
| 123 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.134 ms | 0 - 119 MB | NPU
|
| 124 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® SA7255P | 1.992 ms | 0 - 76 MB | NPU
|
| 125 |
+
| RegNet | TFLITE | w8a8 | Qualcomm® SA8295P | 1.45 ms | 0 - 71 MB | NPU
|
| 126 |
+
| RegNet | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.428 ms | 0 - 81 MB | NPU
|
| 127 |
+
| RegNet | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.993 ms | 0 - 72 MB | NPU
|
| 128 |
+
| RegNet | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.393 ms | 0 - 80 MB | NPU
|
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| 129 |
|
| 130 |
## License
|
| 131 |
* The license for the original implementation of RegNet can be found
|
| 132 |
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
|
| 133 |
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| 134 |
## References
|
| 135 |
* [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)
|
| 136 |
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/regnet.py)
|
| 137 |
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| 138 |
## Community
|
| 139 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 140 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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RegNet_float.dlc
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| 3 |
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RegNet_float.onnx.zip
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tool-versions.yaml
DELETED
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tool_versions:
|
| 2 |
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onnx:
|
| 3 |
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qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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