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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
    • Number of output classes: 21
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 3.304 ms 0 - 146 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 5.214 ms 0 - 12 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.so
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 4.221 ms 11 - 18 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.onnx
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 2.825 ms 0 - 65 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 3.844 ms 1 - 25 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.so
DeepLabV3-Plus-MobileNet-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 3.141 ms 0 - 72 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.onnx
DeepLabV3-Plus-MobileNet-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy TFLITE 14.162 ms 5 - 48 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy QNN 18.291 ms 1 - 9 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized RB5 (Proxy) QCS8250 Proxy TFLITE 127.38 ms 11 - 63 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized QCS8550 (Proxy) QCS8550 Proxy TFLITE 3.315 ms 0 - 8 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized QCS8550 (Proxy) QCS8550 Proxy QNN 3.963 ms 1 - 2 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized SA8255 (Proxy) SA8255P Proxy TFLITE 3.335 ms 0 - 4 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized SA8255 (Proxy) SA8255P Proxy QNN 3.97 ms 1 - 2 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized SA8775 (Proxy) SA8775P Proxy TFLITE 3.294 ms 0 - 9 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized SA8775 (Proxy) SA8775P Proxy QNN 3.994 ms 1 - 2 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized SA8650 (Proxy) SA8650P Proxy TFLITE 3.328 ms 0 - 115 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized SA8650 (Proxy) SA8650P Proxy QNN 3.963 ms 1 - 2 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized QCS8450 (Proxy) QCS8450 Proxy TFLITE 4.166 ms 5 - 71 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized QCS8450 (Proxy) QCS8450 Proxy QNN 5.51 ms 1 - 32 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 2.441 ms 0 - 42 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.tflite
DeepLabV3-Plus-MobileNet-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 3.816 ms 1 - 25 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 2.494 ms 0 - 47 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.onnx
DeepLabV3-Plus-MobileNet-Quantized Snapdragon X Elite CRD Snapdragon® X Elite QNN 4.324 ms 1 - 1 MB INT8 NPU Use Export Script
DeepLabV3-Plus-MobileNet-Quantized Snapdragon X Elite CRD Snapdragon® X Elite ONNX 4.68 ms 17 - 17 MB INT8 NPU DeepLabV3-Plus-MobileNet-Quantized.onnx

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

Profiling Results
------------------------------------------------------------
DeepLabV3-Plus-MobileNet-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 3.3                    
Estimated peak memory usage (MB): [0, 146]               
Total # Ops                     : 104                    
Compute Unit(s)                 : NPU (104 ops)

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