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FFNet-40S

Image Segmentation
PyTorch
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ONNX
real_time
android
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metadata
datasets:
  - cityscapes
library_name: pytorch
license: bsd-3-clause
pipeline_tag: image-segmentation
tags:
  - real_time
  - android

FFNet-40S: Optimized for Mobile Deployment

Semantic segmentation for automotive street scenes

FFNet-40S is a "fuss-free network" that segments street scene images with per-pixel classes like road, sidewalk, and pedestrian. Trained on the Cityscapes dataset.

This model is an implementation of FFNet-40S found here. This repository provides scripts to run FFNet-40S 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: ffnet40S_dBBB_cityscapes_state_dict_quarts
    • Input resolution: 2048x1024
    • Number of parameters: 13.9M
    • Model size: 53.1 MB
    • Number of output classes: 19
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
FFNet-40S Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 17.007 ms 2 - 5 MB FP16 NPU FFNet-40S.tflite
FFNet-40S Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 17.621 ms 25 - 49 MB FP16 NPU FFNet-40S.so
FFNet-40S Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 24.964 ms 26 - 28 MB FP16 NPU FFNet-40S.onnx
FFNet-40S Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 14.889 ms 2 - 102 MB FP16 NPU FFNet-40S.tflite
FFNet-40S Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 15.114 ms 24 - 57 MB FP16 NPU FFNet-40S.so
FFNet-40S Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 22.02 ms 27 - 151 MB FP16 NPU FFNet-40S.onnx
FFNet-40S QCS8550 (Proxy) QCS8550 Proxy TFLITE 16.785 ms 2 - 5 MB FP16 NPU FFNet-40S.tflite
FFNet-40S QCS8550 (Proxy) QCS8550 Proxy QNN 16.327 ms 24 - 25 MB FP16 NPU Use Export Script
FFNet-40S SA8255 (Proxy) SA8255P Proxy TFLITE 16.799 ms 2 - 4 MB FP16 NPU FFNet-40S.tflite
FFNet-40S SA8255 (Proxy) SA8255P Proxy QNN 16.511 ms 24 - 25 MB FP16 NPU Use Export Script
FFNet-40S SA8775 (Proxy) SA8775P Proxy TFLITE 16.774 ms 2 - 5 MB FP16 NPU FFNet-40S.tflite
FFNet-40S SA8775 (Proxy) SA8775P Proxy QNN 16.85 ms 24 - 25 MB FP16 NPU Use Export Script
FFNet-40S SA8650 (Proxy) SA8650P Proxy TFLITE 16.854 ms 2 - 5 MB FP16 NPU FFNet-40S.tflite
FFNet-40S SA8650 (Proxy) SA8650P Proxy QNN 16.816 ms 24 - 25 MB FP16 NPU Use Export Script
FFNet-40S QCS8450 (Proxy) QCS8450 Proxy TFLITE 27.915 ms 2 - 93 MB FP16 NPU FFNet-40S.tflite
FFNet-40S QCS8450 (Proxy) QCS8450 Proxy QNN 28.352 ms 22 - 55 MB FP16 NPU Use Export Script
FFNet-40S Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 11.794 ms 1 - 44 MB FP16 NPU FFNet-40S.tflite
FFNet-40S Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 12.082 ms 24 - 56 MB FP16 NPU Use Export Script
FFNet-40S Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 15.466 ms 32 - 84 MB FP16 NPU FFNet-40S.onnx
FFNet-40S Snapdragon X Elite CRD Snapdragon® X Elite QNN 16.542 ms 24 - 24 MB FP16 NPU Use Export Script
FFNet-40S Snapdragon X Elite CRD Snapdragon® X Elite ONNX 30.278 ms 24 - 24 MB FP16 NPU FFNet-40S.onnx

Installation

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

pip install "qai-hub-models[ffnet_40s]"

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.ffnet_40s.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.ffnet_40s.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.ffnet_40s.export

Profiling Results
------------------------------------------------------------
FFNet-40S
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 17.0                   
Estimated peak memory usage (MB): [2, 5]                 
Total # Ops                     : 92                     
Compute Unit(s)                 : NPU (92 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.ffnet_40s 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.ffnet_40s.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.ffnet_40s.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 FFNet-40S's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of FFNet-40S can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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