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--- |
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library_name: pytorch |
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license: other |
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tags: |
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- real_time |
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- android |
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pipeline_tag: image-segmentation |
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--- |
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# FFNet-78S-LowRes: Optimized for Mobile Deployment |
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## Semantic segmentation for automotive street scenes |
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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. |
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This model is an implementation of FFNet-78S-LowRes found [here](https://github.com/Qualcomm-AI-research/FFNet). |
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This repository provides scripts to run FFNet-78S-LowRes 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_78s_lowres). |
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### Model Details |
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- **Model Type:** Model_use_case.semantic_segmentation |
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- **Model Stats:** |
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- Model checkpoint: ffnet78S_BCC_cityscapes_state_dict_quarts_pre_down |
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- Input resolution: 1024x512 |
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- Number of output classes: 19 |
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- Number of parameters: 26.8M |
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- Model size (float): 102 MB |
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- Model size (w8a8): 26.0 MB |
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| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |
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| FFNet-78S-LowRes | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 49.349 ms | 1 - 166 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 49.478 ms | 5 - 137 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 23.788 ms | 1 - 242 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 24.185 ms | 6 - 171 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 13.719 ms | 1 - 3 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 13.397 ms | 6 - 8 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 7.99 ms | 0 - 51 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.onnx.zip) | |
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| FFNet-78S-LowRes | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 81.494 ms | 1 - 166 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 81.247 ms | 1 - 134 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 49.349 ms | 1 - 166 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 49.478 ms | 5 - 137 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 19.843 ms | 1 - 164 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 19.771 ms | 0 - 131 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 81.494 ms | 1 - 166 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 81.247 ms | 1 - 134 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 9.028 ms | 1 - 250 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 8.969 ms | 6 - 174 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 5.783 ms | 7 - 161 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.onnx.zip) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 7.652 ms | 1 - 165 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 7.566 ms | 6 - 139 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 6.866 ms | 2 - 121 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.onnx.zip) | |
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| FFNet-78S-LowRes | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 6.71 ms | 0 - 173 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.tflite) | |
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| FFNet-78S-LowRes | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 6.695 ms | 6 - 146 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 4.033 ms | 4 - 125 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.onnx.zip) | |
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| FFNet-78S-LowRes | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 14.191 ms | 6 - 6 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.dlc) | |
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| FFNet-78S-LowRes | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.176 ms | 46 - 46 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | TFLITE | 30.495 ms | 0 - 163 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | QNN_DLC | 35.379 ms | 2 - 167 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | ONNX | 117.255 ms | 64 - 80 MB | CPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 9.489 ms | 0 - 30 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | QNN_DLC | 14.754 ms | 1 - 4 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 108.365 ms | 56 - 119 MB | CPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 7.259 ms | 0 - 145 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 10.599 ms | 2 - 148 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 3.491 ms | 0 - 197 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 7.211 ms | 2 - 195 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 2.754 ms | 0 - 2 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.676 ms | 2 - 4 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 2.978 ms | 0 - 29 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 3.267 ms | 0 - 145 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 22.402 ms | 2 - 148 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 7.259 ms | 0 - 145 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 10.599 ms | 2 - 148 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 4.419 ms | 0 - 149 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 6.572 ms | 2 - 154 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 3.267 ms | 0 - 145 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 22.402 ms | 2 - 148 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 2.002 ms | 0 - 198 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 3.311 ms | 2 - 199 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 2.136 ms | 0 - 188 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 1.497 ms | 0 - 149 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 2.265 ms | 2 - 150 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 1.773 ms | 0 - 132 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 4.173 ms | 0 - 162 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | QNN_DLC | 7.346 ms | 2 - 168 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 117.626 ms | 61 - 79 MB | CPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 1.293 ms | 0 - 146 MB | NPU | [FFNet-78S-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.tflite) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 1.846 ms | 2 - 150 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 1.526 ms | 1 - 130 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 5.078 ms | 2 - 2 MB | NPU | [FFNet-78S-LowRes.dlc](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.dlc) | |
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| FFNet-78S-LowRes | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.964 ms | 25 - 25 MB | NPU | [FFNet-78S-LowRes.onnx.zip](https://huggingface.co/qualcomm/FFNet-78S-LowRes/blob/main/FFNet-78S-LowRes_w8a8.onnx.zip) | |
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## Installation |
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Install the package via pip: |
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```bash |
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# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported. |
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pip install "qai-hub-models[ffnet-78s-lowres]" |
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``` |
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## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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_78s_lowres.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_78s_lowres.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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|
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|
```bash |
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python -m qai_hub_models.models.ffnet_78s_lowres.export |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/ffnet_78s_lowres/qai_hub_models/models/FFNet-78S-LowRes/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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|
on-device. Lets go through each step below in detail: |
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|
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|
Step 1: **Compile model for on-device deployment** |
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|
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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|
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|
```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.ffnet_78s_lowres import Model |
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|
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# Load the model |
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|
torch_model = Model.from_pretrained() |
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# Device |
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device = hub.Device("Samsung Galaxy S25") |
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# Trace model |
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input_shape = torch_model.get_input_spec() |
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sample_inputs = torch_model.sample_inputs() |
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()]) |
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# Compile model on a specific device |
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compile_job = hub.submit_compile_job( |
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model=pt_model, |
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device=device, |
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|
input_specs=torch_model.get_input_spec(), |
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|
) |
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# Get target model to run on-device |
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target_model = compile_job.get_target_model() |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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|
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, |
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|
device=device, |
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) |
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|
``` |
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|
Step 3: **Verify on-device accuracy** |
|
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|
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|
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() |
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|
|
|
``` |
|
|
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 Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup). |
|
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|
|
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|
## Run demo on a cloud-hosted device |
|
|
|
|
|
You can also run the demo on-device. |
|
|
|
|
|
```bash |
|
|
python -m qai_hub_models.models.ffnet_78s_lowres.demo --eval-mode on-device |
|
|
``` |
|
|
|
|
|
**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 -- --eval-mode on-device |
|
|
``` |
|
|
|
|
|
|
|
|
## 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. |
|
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|
|
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|
|
|
- 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. |
|
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|
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|
## 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). |
|
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|
## References |
|
|
* [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236) |
|
|
* [Source Model Implementation](https://github.com/Qualcomm-AI-research/FFNet) |
|
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|
## 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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|
