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RegNet

Image Classification
PyTorch
LiteRT
ONNX
backbone
android
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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) QNN 9.871 ms 1 - 10 MB NPU Use Export Script
RegNet float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 2.945 ms 0 - 75 MB NPU RegNet.tflite
RegNet float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 3.574 ms 1 - 44 MB NPU Use Export Script
RegNet float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.006 ms 0 - 13 MB NPU RegNet.tflite
RegNet float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 1.982 ms 1 - 4 MB NPU Use Export Script
RegNet float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 3.288 ms 0 - 66 MB NPU RegNet.tflite
RegNet float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 3.197 ms 1 - 14 MB NPU Use Export Script
RegNet float SA7255P ADP Qualcomm® SA7255P QNN 9.871 ms 1 - 10 MB NPU Use Export Script
RegNet float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 1.995 ms 0 - 35 MB NPU RegNet.tflite
RegNet float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 1.962 ms 1 - 4 MB NPU Use Export Script
RegNet float SA8295P ADP Qualcomm® SA8295P TFLITE 3.524 ms 0 - 63 MB NPU RegNet.tflite
RegNet float SA8295P ADP Qualcomm® SA8295P QNN 3.339 ms 0 - 18 MB NPU Use Export Script
RegNet float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.018 ms 0 - 139 MB NPU RegNet.tflite
RegNet float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 1.946 ms 1 - 3 MB NPU Use Export Script
RegNet float SA8775P ADP Qualcomm® SA8775P TFLITE 3.288 ms 0 - 66 MB NPU RegNet.tflite
RegNet float SA8775P ADP Qualcomm® SA8775P QNN 3.197 ms 1 - 14 MB NPU Use Export Script
RegNet float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.024 ms 0 - 122 MB NPU RegNet.tflite
RegNet float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 1.948 ms 1 - 15 MB NPU Use Export Script
RegNet float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 1.922 ms 0 - 76 MB NPU RegNet.onnx
RegNet float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.403 ms 0 - 78 MB NPU RegNet.tflite
RegNet float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 1.436 ms 80 - 132 MB NPU Use Export Script
RegNet float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.434 ms 0 - 52 MB NPU RegNet.onnx
RegNet float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.351 ms 0 - 69 MB NPU RegNet.tflite
RegNet float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 1.286 ms 0 - 46 MB NPU Use Export Script
RegNet float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 1.391 ms 1 - 47 MB NPU RegNet.onnx
RegNet float Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.169 ms 1 - 1 MB NPU Use Export Script
RegNet float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.987 ms 40 - 40 MB NPU RegNet.onnx
RegNet w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 1.994 ms 0 - 44 MB NPU RegNet.tflite
RegNet w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 2.317 ms 0 - 9 MB NPU Use Export Script
RegNet w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.975 ms 0 - 72 MB NPU RegNet.tflite
RegNet w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 1.407 ms 0 - 62 MB NPU Use Export Script
RegNet w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.758 ms 0 - 60 MB NPU RegNet.tflite
RegNet w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 0.905 ms 0 - 3 MB NPU Use Export Script
RegNet w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.09 ms 0 - 46 MB NPU RegNet.tflite
RegNet w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 1.211 ms 0 - 14 MB NPU Use Export Script
RegNet w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 2.513 ms 0 - 55 MB NPU RegNet.tflite
RegNet w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 3.913 ms 0 - 14 MB NPU Use Export Script
RegNet w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 51.482 ms 3 - 88 MB GPU RegNet.tflite
RegNet w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 1.994 ms 0 - 44 MB NPU RegNet.tflite
RegNet w8a8 SA7255P ADP Qualcomm® SA7255P QNN 2.317 ms 0 - 9 MB NPU Use Export Script
RegNet w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.744 ms 0 - 60 MB NPU RegNet.tflite
RegNet w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 0.918 ms 0 - 2 MB NPU Use Export Script
RegNet w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 1.369 ms 0 - 46 MB NPU RegNet.tflite
RegNet w8a8 SA8295P ADP Qualcomm® SA8295P QNN 1.577 ms 0 - 17 MB NPU Use Export Script
RegNet w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.744 ms 0 - 60 MB NPU RegNet.tflite
RegNet w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 0.912 ms 0 - 2 MB NPU Use Export Script
RegNet w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 1.09 ms 0 - 46 MB NPU RegNet.tflite
RegNet w8a8 SA8775P ADP Qualcomm® SA8775P QNN 1.211 ms 0 - 14 MB NPU Use Export Script
RegNet w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.754 ms 0 - 59 MB NPU RegNet.tflite
RegNet w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 0.925 ms 0 - 19 MB NPU Use Export Script
RegNet w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 3.486 ms 0 - 55 MB NPU RegNet.onnx
RegNet w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.551 ms 0 - 70 MB NPU RegNet.tflite
RegNet w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.645 ms 0 - 62 MB NPU Use Export Script
RegNet w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 2.597 ms 0 - 111 MB NPU RegNet.onnx
RegNet w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.515 ms 0 - 46 MB NPU RegNet.tflite
RegNet w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.586 ms 0 - 51 MB NPU Use Export Script
RegNet w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 2.601 ms 0 - 79 MB NPU RegNet.onnx
RegNet w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 1.079 ms 1 - 1 MB NPU Use Export Script
RegNet w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 3.474 ms 21 - 21 MB NPU RegNet.onnx

Installation

Install the package via pip:

pip install qai-hub-models

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub 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

Profiling Results
------------------------------------------------------------
RegNet
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : QNN                                  
Estimated inference time (ms)   : 9.9                                  
Estimated peak memory usage (MB): [1, 10]                              
Total # Ops                     : 188                                  
Compute Unit(s)                 : npu (188 ops) gpu (0 ops) cpu (0 ops)

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 S24")

# 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. 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 --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 -- --on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared 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.
  • The license for the compiled assets for on-device deployment can be found here

References

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