FFNet-78S-Quantized: Optimized for Mobile Deployment

Semantic segmentation for automotive street scenes

FFNet-78S-Quantized 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-Quantized found here.

This repository provides scripts to run FFNet-78S-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: ffnet78S_dBBB_cityscapes_state_dict_quarts
- Input resolution: 2048x1024
- Number of parameters: 27.5M
- Model size: 26.7 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-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	5.84 ms	1 - 3 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	11.928 ms	0 - 367 MB	INT8	NPU	FFNet-78S-Quantized.onnx
FFNet-78S-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	4.034 ms	1 - 88 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	8.243 ms	7 - 157 MB	INT8	NPU	FFNet-78S-Quantized.onnx
FFNet-78S-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	4.055 ms	0 - 38 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	7.065 ms	2 - 71 MB	INT8	NPU	FFNet-78S-Quantized.onnx
FFNet-78S-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	TFLITE	35.537 ms	0 - 47 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	RB5 (Proxy)	QCS8250 Proxy	TFLITE	219.116 ms	1 - 9 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	5.684 ms	1 - 2 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	SA8255 (Proxy)	SA8255P Proxy	TFLITE	5.742 ms	0 - 2 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	SA8775 (Proxy)	SA8775P Proxy	TFLITE	5.818 ms	1 - 3 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	SA8650 (Proxy)	SA8650P Proxy	TFLITE	5.788 ms	1 - 3 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	SA8295P ADP	SA8295P	TFLITE	11.213 ms	1 - 38 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	7.082 ms	1 - 90 MB	INT8	NPU	FFNet-78S-Quantized.tflite
FFNet-78S-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	12.451 ms	23 - 23 MB	INT8	NPU	FFNet-78S-Quantized.onnx

Installation

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

pip install "qai-hub-models[ffnet_78s_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.ffnet_78s_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.ffnet_78s_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.ffnet_78s_quantized.export

Profiling Results
------------------------------------------------------------
FFNet-78S-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 5.8                    
Estimated peak memory usage (MB): [1, 3]                 
Total # Ops                     : 156                    
Compute Unit(s)                 : NPU (156 ops)

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-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

Community

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