Designing Network Design Spaces
Paper
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2003.13678
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Published
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2
RegNet: Optimized for Mobile Deployment
Imagenet classifier and general purpose backbone
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.
This model is an implementation of RegNet found here.
This repository provides scripts to run RegNet on Qualcomm® devices. More details on model performance across various devices, can be found here.
Model Details
- Model Type: Model_use_case.image_classification
- Model Stats:
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 15.3M
- Model size (float): 58.3 MB
- Model size (w8a8): 15.4 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| RegNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 9.896 ms | 0 - 181 MB | NPU | RegNet.tflite |
| RegNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 9.938 ms | 1 - 162 MB | NPU | RegNet.dlc |
| RegNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 3.267 ms | 0 - 232 MB | NPU | RegNet.tflite |
| RegNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 3.29 ms | 1 - 209 MB | NPU | RegNet.dlc |
| RegNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.983 ms | 0 - 2 MB | NPU | RegNet.tflite |
| RegNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 2.001 ms | 1 - 3 MB | NPU | RegNet.dlc |
| RegNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1.988 ms | 0 - 42 MB | NPU | RegNet.onnx.zip |
| RegNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 13.853 ms | 0 - 181 MB | NPU | RegNet.tflite |
| RegNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 3.311 ms | 1 - 162 MB | NPU | RegNet.dlc |
| RegNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 9.896 ms | 0 - 181 MB | NPU | RegNet.tflite |
| RegNet | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 9.938 ms | 1 - 162 MB | NPU | RegNet.dlc |
| RegNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.991 ms | 0 - 3 MB | NPU | RegNet.tflite |
| RegNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.997 ms | 1 - 2 MB | NPU | RegNet.dlc |
| RegNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 3.452 ms | 0 - 177 MB | NPU | RegNet.tflite |
| RegNet | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 3.444 ms | 1 - 161 MB | NPU | RegNet.dlc |
| RegNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.985 ms | 0 - 2 MB | NPU | RegNet.tflite |
| RegNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 2.012 ms | 1 - 3 MB | NPU | RegNet.dlc |
| RegNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 13.853 ms | 0 - 181 MB | NPU | RegNet.tflite |
| RegNet | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 3.311 ms | 1 - 162 MB | NPU | RegNet.dlc |
| RegNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.368 ms | 0 - 242 MB | NPU | RegNet.tflite |
| RegNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.386 ms | 1 - 214 MB | NPU | RegNet.dlc |
| RegNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.432 ms | 0 - 189 MB | NPU | RegNet.onnx.zip |
| RegNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 1.084 ms | 0 - 184 MB | NPU | RegNet.tflite |
| RegNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 1.086 ms | 1 - 169 MB | NPU | RegNet.dlc |
| RegNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 1.169 ms | 0 - 138 MB | NPU | RegNet.onnx.zip |
| RegNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 0.89 ms | 0 - 185 MB | NPU | RegNet.tflite |
| RegNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 0.9 ms | 1 - 168 MB | NPU | RegNet.dlc |
| RegNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 1.013 ms | 0 - 140 MB | NPU | RegNet.onnx.zip |
| RegNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.235 ms | 1 - 1 MB | NPU | RegNet.dlc |
| RegNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.969 ms | 39 - 39 MB | NPU | RegNet.onnx.zip |
| RegNet | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® Qcm6690 | TFLITE | 6.662 ms | 0 - 169 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® Qcm6690 | QNN_DLC | 7.165 ms | 0 - 175 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® Qcm6690 | ONNX | 17.928 ms | 8 - 24 MB | CPU | RegNet.onnx.zip |
| RegNet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 2.228 ms | 0 - 21 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | QNN_DLC | 2.697 ms | 0 - 2 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 28.317 ms | 7 - 19 MB | CPU | RegNet.onnx.zip |
| RegNet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 2.002 ms | 0 - 167 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 2.269 ms | 0 - 168 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1.152 ms | 0 - 208 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.303 ms | 0 - 204 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 0.728 ms | 0 - 2 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 0.905 ms | 0 - 2 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1.168 ms | 0 - 26 MB | NPU | RegNet.onnx.zip |
| RegNet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.065 ms | 0 - 167 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 1.267 ms | 0 - 169 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 18.249 ms | 0 - 96 MB | GPU | RegNet.tflite |
| RegNet | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | ONNX | 17.539 ms | 5 - 15 MB | CPU | RegNet.onnx.zip |
| RegNet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 2.002 ms | 0 - 167 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 2.269 ms | 0 - 168 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 0.733 ms | 0 - 2 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 0.892 ms | 0 - 3 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 1.418 ms | 0 - 176 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 1.604 ms | 0 - 176 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 0.732 ms | 0 - 2 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 0.891 ms | 0 - 2 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.065 ms | 0 - 167 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 1.267 ms | 0 - 169 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.53 ms | 0 - 213 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.66 ms | 0 - 206 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 0.794 ms | 0 - 200 MB | NPU | RegNet.onnx.zip |
| RegNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 0.45 ms | 0 - 165 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.491 ms | 0 - 169 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 0.701 ms | 0 - 150 MB | NPU | RegNet.onnx.zip |
| RegNet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 0.984 ms | 0 - 169 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | QNN_DLC | 1.174 ms | 0 - 174 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 16.999 ms | 8 - 26 MB | CPU | RegNet.onnx.zip |
| RegNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 0.395 ms | 0 - 169 MB | NPU | RegNet.tflite |
| RegNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 0.446 ms | 0 - 170 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 0.667 ms | 0 - 154 MB | NPU | RegNet.onnx.zip |
| RegNet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.089 ms | 0 - 0 MB | NPU | RegNet.dlc |
| RegNet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.14 ms | 21 - 21 MB | NPU | RegNet.onnx.zip |
Installation
Install the package via pip:
pip install qai-hub-models
Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
Sign-in to Qualcomm® AI Hub Workbench with your
Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.
With this API token, you can configure your client to run models on the cloud hosted devices.
qai-hub configure --api_token API_TOKEN
Navigate to docs for more information.
Demo off target
The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.
python -m qai_hub_models.models.regnet.demo
The above demo runs a reference implementation of pre-processing, model inference, and post processing.
NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).
%run -m qai_hub_models.models.regnet.demo
Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:
- Performance check on-device on a cloud-hosted device
- Downloads compiled assets that can be deployed on-device for Android.
- Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.regnet.export
How does this work?
This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:
Step 1: Compile model for on-device deployment
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the jit.trace and then call the submit_compile_job API.
import torch
import qai_hub as hub
from qai_hub_models.models.regnet import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S25")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
Step 2: Performance profiling on cloud-hosted device
After compiling models from step 1. Models can be profiled model on-device using the
target_model. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
Step 3: Verify on-device accuracy
To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.
Note: This on-device profiling and inference requires access to Qualcomm® AI Hub Workbench. Sign up for access.
Run demo on a cloud-hosted device
You can also run the demo on-device.
python -m qai_hub_models.models.regnet.demo --eval-mode on-device
NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).
%run -m qai_hub_models.models.regnet.demo -- --eval-mode on-device
Deploying compiled model to Android
The models can be deployed using multiple runtimes:
TensorFlow Lite (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application.QNN (
.soexport ): This sample app provides instructions on how to use the.soshared library in an Android application.
View on Qualcomm® AI Hub
Get more details on RegNet's performance across various devices here. Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of RegNet can be found here.
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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