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--- |
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datasets: |
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- cityscapes |
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library_name: pytorch |
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license: bsd-3-clause |
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pipeline_tag: image-segmentation |
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tags: |
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- quantized |
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- real_time |
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- android |
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--- |
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# FFNet-54S-Quantized: Optimized for Mobile Deployment |
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## Semantic segmentation for automotive street scenes |
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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. |
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This model is an implementation of FFNet-54S-Quantized found [here](https://github.com/Qualcomm-AI-research/FFNet). |
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This repository provides scripts to run FFNet-54S-Quantized on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/ffnet_54s_quantized). |
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### Model Details |
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- **Model Type:** Semantic segmentation |
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- **Model Stats:** |
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- Model checkpoint: ffnet54S_dBBB_cityscapes_state_dict_quarts |
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- Input resolution: 2048x1024 |
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- Number of parameters: 18.0M |
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- Model size: 17.5 MB |
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| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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| ---|---|---|---|---|---|---|---| |
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| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 7.119 ms | 1 - 2 MB | INT8 | NPU | [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) |
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## Installation |
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This model can be installed as a Python package via pip. |
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```bash |
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pip install "qai-hub-models[ffnet_54s_quantized]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.ffnet_54s_quantized.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.ffnet_54s_quantized.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.ffnet_54s_quantized.export |
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``` |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN (`.so` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on FFNet-54S-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_54s_quantized). |
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) |
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## License |
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- The license for the original implementation of FFNet-54S-Quantized can be found |
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[here](https://github.com/Qualcomm-AI-research/FFNet/blob/master/LICENSE). |
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- 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) |
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## References |
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* [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236) |
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* [Source Model Implementation](https://github.com/Qualcomm-AI-research/FFNet) |
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## Community |
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* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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