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DeepLabV3-Plus-MobileNet

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

DeepLabV3-Plus-MobileNet: Optimized for Mobile Deployment

Deep Convolutional Neural Network model for semantic segmentation

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

This model is an implementation of DeepLabV3-Plus-MobileNet found here. This repository provides scripts to run DeepLabV3-Plus-MobileNet 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: 22.2 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 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 13.441 ms 21 - 22 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 13.124 ms 3 - 20 MB FP16 NPU DeepLabV3-Plus-MobileNet.so
DeepLabV3-Plus-MobileNet Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 16.946 ms 46 - 330 MB FP16 NPU DeepLabV3-Plus-MobileNet.onnx
DeepLabV3-Plus-MobileNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 10.784 ms 21 - 98 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 10.749 ms 3 - 28 MB FP16 NPU DeepLabV3-Plus-MobileNet.so
DeepLabV3-Plus-MobileNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 15.136 ms 1 - 82 MB FP16 NPU DeepLabV3-Plus-MobileNet.onnx
DeepLabV3-Plus-MobileNet QCS8550 (Proxy) QCS8550 Proxy TFLITE 13.166 ms 21 - 65 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet QCS8550 (Proxy) QCS8550 Proxy QNN 12.047 ms 3 - 4 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet SA8255 (Proxy) SA8255P Proxy TFLITE 13.288 ms 21 - 33 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet SA8255 (Proxy) SA8255P Proxy QNN 12.206 ms 3 - 4 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet SA8775 (Proxy) SA8775P Proxy TFLITE 13.223 ms 14 - 19 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet SA8775 (Proxy) SA8775P Proxy QNN 12.296 ms 3 - 4 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet SA8650 (Proxy) SA8650P Proxy TFLITE 13.234 ms 27 - 29 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet SA8650 (Proxy) SA8650P Proxy QNN 12.164 ms 3 - 4 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet QCS8450 (Proxy) QCS8450 Proxy TFLITE 18.816 ms 21 - 97 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet QCS8450 (Proxy) QCS8450 Proxy QNN 18.643 ms 3 - 30 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 7.831 ms 19 - 56 MB FP16 NPU DeepLabV3-Plus-MobileNet.tflite
DeepLabV3-Plus-MobileNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 9.188 ms 3 - 26 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 11.971 ms 51 - 90 MB FP16 NPU DeepLabV3-Plus-MobileNet.onnx
DeepLabV3-Plus-MobileNet Snapdragon X Elite CRD Snapdragon® X Elite QNN 12.38 ms 3 - 3 MB FP16 NPU Use Export Script
DeepLabV3-Plus-MobileNet Snapdragon X Elite CRD Snapdragon® X Elite ONNX 16.661 ms 66 - 66 MB FP16 NPU DeepLabV3-Plus-MobileNet.onnx

Installation

This model can be installed as a Python 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.deeplabv3_plus_mobilenet.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.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.export

Profiling Results
------------------------------------------------------------
DeepLabV3-Plus-MobileNet
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 13.4                   
Estimated peak memory usage (MB): [21, 22]               
Total # Ops                     : 98                     
Compute Unit(s)                 : NPU (98 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.deeplabv3_plus_mobilenet import 

# Load the model

# Device
device = hub.Device("Samsung Galaxy S23")

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.deeplabv3_plus_mobilenet.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.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'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 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community