FFNet-78S: Optimized for Mobile Deployment

Semantic segmentation for automotive street scenes

FFNet-78S 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-78S found here.

This repository provides scripts to run FFNet-78S 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: ffnet78S_dBBB_cityscapes_state_dict_quarts
- Input resolution: 2048x1024
- Number of parameters: 27.5M
- Model size: 105 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-78S	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	23.72 ms	2 - 30 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	24.001 ms	24 - 52 MB	FP16	NPU	FFNet-78S.so
FFNet-78S	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	37.609 ms	24 - 54 MB	FP16	NPU	FFNet-78S.onnx
FFNet-78S	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	17.32 ms	1 - 44 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	17.928 ms	20 - 59 MB	FP16	NPU	FFNet-78S.so
FFNet-78S	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	26.395 ms	32 - 178 MB	FP16	NPU	FFNet-78S.onnx
FFNet-78S	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	14.248 ms	1 - 43 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	16.848 ms	20 - 60 MB	FP16	NPU	Use Export Script
FFNet-78S	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	25.427 ms	28 - 86 MB	FP16	NPU	FFNet-78S.onnx
FFNet-78S	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	23.647 ms	2 - 30 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	QCS8550 (Proxy)	QCS8550 Proxy	QNN	23.086 ms	24 - 25 MB	FP16	NPU	Use Export Script
FFNet-78S	SA7255P ADP	SA7255P	TFLITE	1164.068 ms	0 - 40 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	SA7255P ADP	SA7255P	QNN	1162.892 ms	24 - 30 MB	FP16	NPU	Use Export Script
FFNet-78S	SA8255 (Proxy)	SA8255P Proxy	TFLITE	23.214 ms	2 - 32 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	SA8255 (Proxy)	SA8255P Proxy	QNN	24.106 ms	24 - 25 MB	FP16	NPU	Use Export Script
FFNet-78S	SA8295P ADP	SA8295P	TFLITE	44.303 ms	2 - 34 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	SA8295P ADP	SA8295P	QNN	43.366 ms	24 - 30 MB	FP16	NPU	Use Export Script
FFNet-78S	SA8650 (Proxy)	SA8650P Proxy	TFLITE	23.216 ms	2 - 32 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	SA8650 (Proxy)	SA8650P Proxy	QNN	23.393 ms	24 - 25 MB	FP16	NPU	Use Export Script
FFNet-78S	SA8775P ADP	SA8775P	TFLITE	49.885 ms	2 - 42 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	SA8775P ADP	SA8775P	QNN	49.835 ms	24 - 29 MB	FP16	NPU	Use Export Script
FFNet-78S	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	39.548 ms	2 - 41 MB	FP16	NPU	FFNet-78S.tflite
FFNet-78S	QCS8450 (Proxy)	QCS8450 Proxy	QNN	39.617 ms	24 - 58 MB	FP16	NPU	Use Export Script
FFNet-78S	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	23.164 ms	24 - 24 MB	FP16	NPU	Use Export Script
FFNet-78S	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	36.863 ms	31 - 31 MB	FP16	NPU	FFNet-78S.onnx

Installation

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

pip install "qai-hub-models[ffnet_78s]"

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_78s.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_78s.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_78s.export

Profiling Results
------------------------------------------------------------
FFNet-78S
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 23.7                   
Estimated peak memory usage (MB): [2, 30]                
Total # Ops                     : 149                    
Compute Unit(s)                 : NPU (149 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_78s import Model

# Load the model
torch_model = Model.from_pretrained()

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

# 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.ffnet_78s.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_78s.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-78S's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of FFNet-78S can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

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