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---
datasets:
- cityscapes
library_name: pytorch
license: bsd-3-clause
pipeline_tag: image-segmentation
tags:
- quantized
- real_time
- android
---
# 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]({source_repo}).
This repository provides scripts to run FFNet-78S-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/ffnet_78s_quantized).
### 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 | 5.745 ms | 0 - 2 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 11.963 ms | 0 - 24 MB | INT8 | NPU | [FFNet-78S-Quantized.onnx](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.onnx) |
| FFNet-78S-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.089 ms | 1 - 86 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 8.58 ms | 5 - 151 MB | INT8 | NPU | [FFNet-78S-Quantized.onnx](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.onnx) |
| FFNet-78S-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 35.597 ms | 1 - 48 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 218.427 ms | 1 - 3 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 5.683 ms | 1 - 2 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 5.752 ms | 1 - 3 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 5.787 ms | 0 - 2 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 5.717 ms | 1 - 3 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 7.035 ms | 1 - 90 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.501 ms | 1 - 38 MB | INT8 | NPU | [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) |
| FFNet-78S-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 8.055 ms | 7 - 76 MB | INT8 | NPU | [FFNet-78S-Quantized.onnx](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.onnx) |
| FFNet-78S-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 12.351 ms | 22 - 22 MB | INT8 | NPU | [FFNet-78S-Quantized.onnx](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.onnx) |
## Installation
This model can be installed as a Python package via pip.
```bash
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](https://app.aihub.qualcomm.com/) 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.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/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.
```bash
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.
```bash
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) : 5.7
Estimated peak memory usage (MB): [0, 2]
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](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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](https://aihub.qualcomm.com/models/ffnet_78s_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of FFNet-78S-Quantized can be found [here](https://github.com/Qualcomm-AI-research/FFNet/blob/master/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236)
* [Source Model Implementation](https://github.com/Qualcomm-AI-research/FFNet)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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