v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- MNASNet05_float.dlc +0 -3
- MNASNet05_float.onnx.zip +0 -3
- MNASNet05_float.tflite +0 -3
- MNASNet05_w8a16.dlc +0 -3
- MNASNet05_w8a16.onnx.zip +0 -3
- README.md +99 -249
- tool-versions.yaml +0 -4
MNASNet05_float.dlc
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README.md
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# MNASNet05: Optimized for
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## Imagenet classifier and general purpose backbone
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MNASNet05 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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The package contains a simple end-to-end demo that downloads pre-trained
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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.mnasnet05.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.mnasnet05.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.mnasnet05.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/mnasnet05/qai_hub_models/models/MNASNet05/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.mnasnet05 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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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.mnasnet05.demo --eval-mode on-device
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```
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environment, please add the following to your cell (instead of the above).
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%run -m qai_hub_models.models.mnasnet05.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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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 MNASNet05's performance across various devices [here](https://aihub.qualcomm.com/models/mnasnet05).
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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 MNASNet05 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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* [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)
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* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.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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# MNASNet05: Optimized for Qualcomm Devices
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MNASNet05 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 MNASNet05 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.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/mnasnet05) 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/mnasnet05/releases/v0.46.1/mnasnet05-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/mnasnet05/releases/v0.46.1/mnasnet05-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/mnasnet05/releases/v0.46.1/mnasnet05-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/mnasnet05/releases/v0.46.1/mnasnet05-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/mnasnet05/releases/v0.46.1/mnasnet05-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[MNASNet05 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/mnasnet05)**.
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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/mnasnet05) 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 [MNASNet05 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mnasnet05) 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: 2.21M
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- Model size (float): 8.45 MB
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- Model size (w8a16): 2.79 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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| MNASNet05 | ONNX | float | Snapdragon® X Elite | 0.617 ms | 5 - 5 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.493 ms | 0 - 116 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.691 ms | 0 - 12 MB | NPU
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| MNASNet05 | ONNX | float | Qualcomm® QCS9075 | 0.97 ms | 1 - 3 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.381 ms | 0 - 99 MB | NPU
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| MNASNet05 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.317 ms | 1 - 99 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® X Elite | 0.645 ms | 2 - 2 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.521 ms | 0 - 111 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS6490 | 29.306 ms | 8 - 11 MB | CPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.705 ms | 0 - 44 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCS9075 | 0.899 ms | 0 - 3 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Qualcomm® QCM6690 | 18.804 ms | 9 - 16 MB | CPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.368 ms | 0 - 100 MB | NPU
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| MNASNet05 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 13.001 ms | 9 - 16 MB | CPU
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| 80 |
+
| MNASNet05 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.329 ms | 0 - 100 MB | NPU
|
| 81 |
+
| MNASNet05 | QNN_DLC | float | Snapdragon® X Elite | 0.916 ms | 1 - 1 MB | NPU
|
| 82 |
+
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.516 ms | 0 - 46 MB | NPU
|
| 83 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 2.318 ms | 1 - 30 MB | NPU
|
| 84 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.775 ms | 1 - 2 MB | NPU
|
| 85 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8775P | 1.091 ms | 1 - 31 MB | NPU
|
| 86 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS9075 | 0.974 ms | 1 - 3 MB | NPU
|
| 87 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.579 ms | 0 - 48 MB | NPU
|
| 88 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® SA7255P | 2.318 ms | 1 - 30 MB | NPU
|
| 89 |
+
| MNASNet05 | QNN_DLC | float | Qualcomm® SA8295P | 1.416 ms | 0 - 28 MB | NPU
|
| 90 |
+
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.379 ms | 0 - 33 MB | NPU
|
| 91 |
+
| MNASNet05 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.291 ms | 1 - 33 MB | NPU
|
| 92 |
+
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.924 ms | 0 - 0 MB | NPU
|
| 93 |
+
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.53 ms | 0 - 37 MB | NPU
|
| 94 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 2.225 ms | 2 - 4 MB | NPU
|
| 95 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 1.665 ms | 0 - 26 MB | NPU
|
| 96 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.785 ms | 0 - 2 MB | NPU
|
| 97 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 4.096 ms | 0 - 27 MB | NPU
|
| 98 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 0.925 ms | 0 - 2 MB | NPU
|
| 99 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 3.052 ms | 0 - 138 MB | NPU
|
| 100 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 0.958 ms | 0 - 39 MB | NPU
|
| 101 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.665 ms | 0 - 26 MB | NPU
|
| 102 |
+
| MNASNet05 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.235 ms | 0 - 23 MB | NPU
|
| 103 |
+
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.361 ms | 0 - 25 MB | NPU
|
| 104 |
+
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.785 ms | 0 - 24 MB | NPU
|
| 105 |
+
| MNASNet05 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.29 ms | 0 - 29 MB | NPU
|
| 106 |
+
| MNASNet05 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.519 ms | 0 - 47 MB | NPU
|
| 107 |
+
| MNASNet05 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 2.331 ms | 0 - 30 MB | NPU
|
| 108 |
+
| MNASNet05 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.776 ms | 0 - 1 MB | NPU
|
| 109 |
+
| MNASNet05 | TFLITE | float | Qualcomm® SA8775P | 1.107 ms | 0 - 33 MB | NPU
|
| 110 |
+
| MNASNet05 | TFLITE | float | Qualcomm® QCS9075 | 0.978 ms | 0 - 8 MB | NPU
|
| 111 |
+
| MNASNet05 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.581 ms | 0 - 49 MB | NPU
|
| 112 |
+
| MNASNet05 | TFLITE | float | Qualcomm® SA7255P | 2.331 ms | 0 - 30 MB | NPU
|
| 113 |
+
| MNASNet05 | TFLITE | float | Qualcomm® SA8295P | 1.432 ms | 0 - 29 MB | NPU
|
| 114 |
+
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.384 ms | 0 - 35 MB | NPU
|
| 115 |
+
| MNASNet05 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.29 ms | 0 - 34 MB | NPU
|
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| 116 |
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| 117 |
## License
|
| 118 |
* The license for the original implementation of MNASNet05 can be found
|
| 119 |
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
|
| 120 |
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| 121 |
## References
|
| 122 |
* [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)
|
| 123 |
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py)
|
| 124 |
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| 125 |
## Community
|
| 126 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 127 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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tool-versions.yaml
DELETED
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@@ -1,4 +0,0 @@
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|
| 1 |
-
tool_versions:
|
| 2 |
-
onnx:
|
| 3 |
-
qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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