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FFNet-54S

Image Segmentation
PyTorch
LiteRT
ONNX
android
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  FFNet-54S 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 found [here](https://github.com/Qualcomm-AI-research/FFNet).
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  This repository provides scripts to run FFNet-54S 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).
@@ -32,15 +32,32 @@ More details on model performance across various devices, can be found
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  - Model size: 68.8 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 | 19.62 ms | 2 - 4 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 20.29 ms | 24 - 48 MB | FP16 | NPU | [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.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_54s.export
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  ```
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-
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  ```
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- Profile Job summary of FFNet-54S
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 20.26 ms
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- Estimated Peak Memory Range: 24.05-24.05 MB
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- Compute Units: NPU (175) | Total (175)
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-
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  ```
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  Get more details on FFNet-54S's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_54s).
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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 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:ai-hub-support@qti.qualcomm.com).
 
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  FFNet-54S 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 found [here]({source_repo}).
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  This repository provides scripts to run FFNet-54S 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).
 
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  - Model size: 68.8 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-54S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 19.975 ms | 2 - 4 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 20.164 ms | 24 - 45 MB | FP16 | NPU | [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) |
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+ | FFNet-54S | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 28.053 ms | 25 - 27 MB | FP16 | NPU | [FFNet-54S.onnx](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.onnx) |
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+ | FFNet-54S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 17.729 ms | 2 - 115 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 17.811 ms | 20 - 54 MB | FP16 | NPU | [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) |
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+ | FFNet-54S | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 24.675 ms | 1 - 136 MB | FP16 | NPU | [FFNet-54S.onnx](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.onnx) |
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+ | FFNet-54S | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 19.858 ms | 2 - 7 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 18.98 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 19.96 ms | 2 - 5 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | SA8255 (Proxy) | SA8255P Proxy | QNN | 19.252 ms | 24 - 26 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 19.841 ms | 2 - 4 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | SA8775 (Proxy) | SA8775P Proxy | QNN | 19.187 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 20.016 ms | 2 - 4 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | SA8650 (Proxy) | SA8650P Proxy | QNN | 19.257 ms | 24 - 25 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 32.162 ms | 2 - 99 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 32.442 ms | 24 - 56 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 14.186 ms | 0 - 47 MB | FP16 | NPU | [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) |
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+ | FFNet-54S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 11.828 ms | 24 - 60 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 22.041 ms | 30 - 81 MB | FP16 | NPU | [FFNet-54S.onnx](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.onnx) |
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+ | FFNet-54S | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 19.271 ms | 24 - 24 MB | FP16 | NPU | Use Export Script |
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+ | FFNet-54S | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 32.787 ms | 24 - 24 MB | FP16 | NPU | [FFNet-54S.onnx](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.ffnet_54s.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ FFNet-54S
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 20.0
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+ Estimated peak memory usage (MB): [2, 4]
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+ Total # Ops : 113
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+ Compute Unit(s) : NPU (113 ops)
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  ```
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  Get more details on FFNet-54S's performance across various devices [here](https://aihub.qualcomm.com/models/ffnet_54s).
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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-54S 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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  ## 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:ai-hub-support@qti.qualcomm.com).