FFNet-78S-LowRes / README.md

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	---
	datasets:
	- cityscapes
	library_name: pytorch
	license: bsd-3-clause
	pipeline_tag: image-segmentation
	tags:
	- real_time
	- android

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ffnet_78s_lowres/web-assets/model_demo.png)

	# FFNet-78S-LowRes: Optimized for Mobile Deployment
	## Semantic segmentation for automotive street scenes

	FFNet-78S-LowRes 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-LowRes found [here](https://github.com/Qualcomm-AI-research/FFNet).
	This repository provides scripts to run FFNet-78S-LowRes on Qualcomm® devices.
	More details on model performance across various devices, can be found
	[here](https://aihub.qualcomm.com/models/ffnet_78s_lowres).


	### Model Details

	- Model Type: Semantic segmentation
	- Model Stats:
	- Model checkpoint: ffnet78S_BCC_cityscapes_state_dict_quarts_pre_down
	- Input resolution: 1024x512
	- Number of parameters: 26.8M
	- Model size: 102 MB




	\| 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 \| 10.698 ms \| 0 - 8 MB \| FP16 \| NPU \| [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite)
	\| Samsung Galaxy S23 Ultra (Android 13) \| Snapdragon® 8 Gen 2 \| QNN Model Library \| 11.228 ms \| 2 - 53 MB \| FP16 \| NPU \| [FFNet-78S-LowRes.so](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.so)



	## Installation

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

	```bash
	pip install "qai-hub-models[ffnet_78s_lowres]"
	```



	## 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_lowres.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_lowres.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_lowres.export
	```

	```
	Profile Job summary of FFNet-78S-LowRes
	--------------------------------------------------
	Device: Snapdragon X Elite CRD (11)
	Estimated Inference Time: 20.53 ms
	Estimated Peak Memory Range: 6.01-6.01 MB
	Compute Units: NPU (236) \| Total (236)


	```


	## How does this work?

	This [export script](https://aihub.qualcomm.com/models/ffnet_78s_lowres/qai_hub_models/models/FFNet-78S-LowRes/export.py)
	leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
	on-device. Lets go through each step below in detail:

	Step 1: Compile model for on-device deployment

	To compile a PyTorch model for on-device deployment, we first trace the model
	in memory using the `jit.trace` and then call the `submit_compile_job` API.

	```python
	import torch

	import qai_hub as hub
	from qai_hub_models.models.ffnet_78s_lowres import Model

	# Load the model
	torch_model = Model.from_pretrained()
	torch_model.eval()

	# Device
	device = hub.Device("Samsung Galaxy S23")

	# Trace model
	input_shape = torch_model.get_input_spec()
	sample_inputs = torch_model.sample_inputs()

	pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

	# Compile model on a specific device
	compile_job = hub.submit_compile_job(
	model=pt_model,
	device=device,
	input_specs=torch_model.get_input_spec(),
	)

	# Get target model to run on-device
	target_model = compile_job.get_target_model()

	```


	Step 2: Performance profiling on cloud-hosted device

	After compiling models from step 1. Models can be profiled model on-device using the
	`target_model`. Note that this scripts runs the model on a device automatically
	provisioned in the cloud. Once the job is submitted, you can navigate to a
	provided job URL to view a variety of on-device performance metrics.
	```python
	profile_job = hub.submit_profile_job(
	model=target_model,
	device=device,
	)

	```

	Step 3: Verify on-device accuracy

	To verify the accuracy of the model on-device, you can run on-device inference
	on sample input data on the same cloud hosted device.
	```python
	input_data = torch_model.sample_inputs()
	inference_job = hub.submit_inference_job(
	model=target_model,
	device=device,
	inputs=input_data,
	)

	on_device_output = inference_job.download_output_data()

	```
	With the output of the model, you can compute like PSNR, relative errors or
	spot check the output with expected output.

	Note: This on-device profiling and inference requires access to Qualcomm®
	AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).




	## 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-LowRes's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_78s_lowres).
	Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)

	## License
	- The license for the original implementation of FFNet-78S-LowRes 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://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.
	* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).