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DeepLabV3-Plus-MobileNet-Quantized

Image Segmentation
PyTorch
TF Lite
ONNX
quantized
android
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metadata
datasets:
  - VOC2012
library_name: pytorch
license: mit
pipeline_tag: image-segmentation
tags:
  - quantized
  - android

DeepLabV3-Plus-MobileNet-Quantized: Optimized for Mobile Deployment

Quantized Deep Convolutional Neural Network model for semantic segmentation

DeepLabV3 Quantized is designed for semantic segmentation at multiple scales, trained on various datasets. It uses MobileNet as a backbone.

This model is an implementation of DeepLabV3-Plus-MobileNet-Quantized found here. This repository provides scripts to run DeepLabV3-Plus-MobileNet-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Semantic segmentation
  • Model Stats:
    • Model checkpoint: VOC2012
    • Input resolution: 513x513
    • Number of parameters: 5.80M
    • Model size: 6.04 MB
Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 TFLite 3.613 ms 0 - 2 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 QNN Model Library 5.334 ms 1 - 7 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.so

Installation

This model can be installed as a Python package via pip.

pip install "qai-hub-models[deeplabv3_plus_mobilenet_quantized]"

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.deeplabv3_plus_mobilenet_quantized.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.deeplabv3_plus_mobilenet_quantized.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.deeplabv3_plus_mobilenet_quantized.export

Profile Job summary of DeepLabV3-Plus-MobileNet-Quantized
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 4.51 ms
Estimated Peak Memory Range: 0.78-0.78 MB
Compute Units: NPU (100) | Total (100)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.deeplabv3_plus_mobilenet_quantized.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.deeplabv3_plus_mobilenet_quantized.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 DeepLabV3-Plus-MobileNet-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of DeepLabV3-Plus-MobileNet-Quantized can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community