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Sponsored by
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qualcomm / FFNet-78S-Quantized
Model's Last Updated: Abril 10 2025
image-segmentation
Introduction of FFNet-78S-Quantized
Model Details of FFNet-78S-Quantized
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
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 5.721 ms | 0 - 2 MB | INT8 | NPU | FFNet-78S-Quantized.tflite |
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
Deploying compiled model to Android
The models can be deployed using multiple runtimes:
-
TensorFlow Lite (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application. -
QNN (
.soexport ): This sample app provides instructions on how to use the.soshared 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
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us .
Runs of qualcomm FFNet-78S-Quantized on huggingface.co
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More Information About FFNet-78S-Quantized huggingface.co Model
Url of FFNet-78S-Quantized
FFNet-78S-Quantized huggingface.co Url
Provider of FFNet-78S-Quantized huggingface.co
Other API from qualcomm
Updated:Abril 08 2026
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