Hugging Face's logo

qualcomm
/
FFNet-54S-Quantized

Image Segmentation
PyTorch
TF Lite
ONNX
quantized
real_time
android
Model card Files Community
FFNet-54S-Quantized / README.md
qaihm-bot's picture
qaihm-bot
Upload README.md with huggingface_hub
3f55261 verified
preview code
|
raw
history blame
8.46 kB
metadata
datasets:
  - cityscapes
library_name: pytorch
license: bsd-3-clause
pipeline_tag: image-segmentation
tags:
  - quantized
  - real_time
  - android

FFNet-54S-Quantized: Optimized for Mobile Deployment

Semantic segmentation for automotive street scenes

FFNet-54S-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-54S-Quantized found here. This repository provides scripts to run FFNet-54S-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: ffnet54S_dBBB_cityscapes_state_dict_quarts
    • Input resolution: 2048x1024
    • Number of parameters: 18.0M
    • Model size: 17.5 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-54S-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 4.79 ms 1 - 3 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 11.064 ms 0 - 16 MB INT8 NPU FFNet-54S-Quantized.onnx
FFNet-54S-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 3.364 ms 0 - 72 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 7.967 ms 4 - 124 MB INT8 NPU FFNet-54S-Quantized.onnx
FFNet-54S-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy TFLITE 31.917 ms 1 - 45 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized RB5 (Proxy) QCS8250 Proxy TFLITE 201.523 ms 1 - 3 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized QCS8550 (Proxy) QCS8550 Proxy TFLITE 4.692 ms 1 - 13 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized SA8255 (Proxy) SA8255P Proxy TFLITE 4.711 ms 1 - 3 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized SA8775 (Proxy) SA8775P Proxy TFLITE 4.773 ms 0 - 18 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized SA8650 (Proxy) SA8650P Proxy TFLITE 4.698 ms 1 - 12 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized QCS8450 (Proxy) QCS8450 Proxy TFLITE 5.95 ms 1 - 76 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 2.871 ms 1 - 34 MB INT8 NPU FFNet-54S-Quantized.tflite
FFNet-54S-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 7.316 ms 7 - 65 MB INT8 NPU FFNet-54S-Quantized.onnx
FFNet-54S-Quantized Snapdragon X Elite CRD Snapdragon® X Elite ONNX 11.112 ms 13 - 13 MB INT8 NPU FFNet-54S-Quantized.onnx

Installation

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

pip install "qai-hub-models[ffnet_54s_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_54s_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_54s_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_54s_quantized.export

Profiling Results
------------------------------------------------------------
FFNet-54S-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 4.8                    
Estimated peak memory usage (MB): [1, 3]                 
Total # Ops                     : 120                    
Compute Unit(s)                 : NPU (120 ops)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.ffnet_54s_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.ffnet_54s_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 FFNet-54S-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of FFNet-54S-Quantized can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community