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FFNet-40S

Image Segmentation
PyTorch
LiteRT
ONNX
real_time
android
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  FFNet-40S 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-40S found [here](https://github.com/Qualcomm-AI-research/FFNet).
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  This repository provides scripts to run FFNet-40S 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_40s).
@@ -33,15 +33,32 @@ More details on model performance across various devices, can be found
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  - Model size: 53.1 MB
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  - Number of output classes: 19
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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 | 17.077 ms | 2 - 4 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 17.566 ms | 26 - 40 MB | FP16 | NPU | [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so)
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-
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-
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.ffnet_40s.export
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  ```
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-
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  ```
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- Profile Job summary of FFNet-40S
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 17.86 ms
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- Estimated Peak Memory Range: 24.05-24.05 MB
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- Compute Units: NPU (140) | Total (140)
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-
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  ```
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  Get more details on FFNet-40S's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_40s).
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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-40S 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://aihub.qualcomm.com/community/slack) 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:[email protected]).
 
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  FFNet-40S 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-40S found [here]({source_repo}).
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  This repository provides scripts to run FFNet-40S 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_40s).
 
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  - Model size: 53.1 MB
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  - Number of output classes: 19
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+ | Model | 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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+ | FFNet-40S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 17.007 ms | 2 - 5 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 17.621 ms | 25 - 49 MB | FP16 | NPU | [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) |
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+ | FFNet-40S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 24.964 ms | 26 - 28 MB | FP16 | NPU | [FFNet-40S.onnx](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.onnx) |
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+ | FFNet-40S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 14.889 ms | 2 - 102 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 15.114 ms | 24 - 57 MB | FP16 | NPU | [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) |
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+ | FFNet-40S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 22.02 ms | 27 - 151 MB | FP16 | NPU | [FFNet-40S.onnx](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.onnx) |
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+ | FFNet-40S | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 16.785 ms | 2 - 5 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 16.327 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 16.799 ms | 2 - 4 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | SA8255 (Proxy) | SA8255P Proxy | QNN | 16.511 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 16.774 ms | 2 - 5 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | SA8775 (Proxy) | SA8775P Proxy | QNN | 16.85 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 16.854 ms | 2 - 5 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | SA8650 (Proxy) | SA8650P Proxy | QNN | 16.816 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 27.915 ms | 2 - 93 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 28.352 ms | 22 - 55 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 11.794 ms | 1 - 44 MB | FP16 | NPU | [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) |
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+ | FFNet-40S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 12.082 ms | 24 - 56 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 15.466 ms | 32 - 84 MB | FP16 | NPU | [FFNet-40S.onnx](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.onnx) |
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+ | FFNet-40S | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 16.542 ms | 24 - 24 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-40S | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 30.278 ms | 24 - 24 MB | FP16 | NPU | [FFNet-40S.onnx](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.ffnet_40s.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ FFNet-40S
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 17.0
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+ Estimated peak memory usage (MB): [2, 5]
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+ Total # Ops : 92
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+ Compute Unit(s) : NPU (92 ops)
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  ```
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  Get more details on FFNet-40S's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_40s).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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+
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  ## License
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+ * The license for the original implementation of FFNet-40S can be found [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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+
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+
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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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+
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) 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:[email protected]).