---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-segmentation

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/web-assets/model_demo.png)

# FCN-ResNet50: Optimized for Qualcomm Devices

FCN_ResNet50 is a machine learning model that can segment images from the COCO dataset. It uses ResNet50 as a backbone.

This is based on the implementation of FCN-ResNet50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/fcn.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/fcn_resnet50) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.50.2/fcn_resnet50-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[FCN-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/fcn_resnet50)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/fcn_resnet50) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [FCN-ResNet50 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/fcn_resnet50) for usage instructions.

## Model Details

**Model Type:** Model_use_case.semantic_segmentation

**Model Stats:**
- Model checkpoint: COCO_WITH_VOC_LABELS_V1
- Input resolution: 224x224
- Number of output classes: 21
- Number of parameters: 33.0M
- Model size (float): 126 MB
- Model size (w8a8): 32.2 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 22.429 ms | 4 - 326 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® X2 Elite | 23.176 ms | 63 - 63 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® X Elite | 43.168 ms | 62 - 62 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 32.577 ms | 0 - 382 MB | NPU
| FCN-ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 43.03 ms | 3 - 6 MB | NPU
| FCN-ResNet50 | ONNX | float | Qualcomm® QCS9075 | 75.156 ms | 3 - 9 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.466 ms | 1 - 297 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.055 ms | 0 - 248 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X2 Elite | 7.077 ms | 33 - 33 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X Elite | 13.821 ms | 32 - 32 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 10.174 ms | 0 - 279 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS6490 | 907.251 ms | 68 - 111 MB | CPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 13.515 ms | 1 - 259 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS9075 | 15.155 ms | 1 - 4 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCM6690 | 843.043 ms | 54 - 63 MB | CPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.58 ms | 1 - 206 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 707.962 ms | 80 - 88 MB | CPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 21.662 ms | 3 - 331 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X2 Elite | 22.829 ms | 3 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 44.195 ms | 3 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 33.24 ms | 3 - 385 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 272.365 ms | 1 - 304 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 44.069 ms | 3 - 6 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 72.498 ms | 1 - 304 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 78.663 ms | 3 - 8 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 83.904 ms | 1 - 275 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 272.365 ms | 1 - 304 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 77.755 ms | 0 - 217 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.24 ms | 3 - 322 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.542 ms | 1 - 246 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 7.864 ms | 1 - 1 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X Elite | 15.175 ms | 1 - 1 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.375 ms | 1 - 267 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 92.908 ms | 1 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 39.543 ms | 1 - 205 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.947 ms | 1 - 197 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 14.759 ms | 1 - 215 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 17.017 ms | 3 - 5 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 392.044 ms | 1 - 347 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 23.99 ms | 1 - 267 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 39.543 ms | 1 - 205 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 22.112 ms | 1 - 209 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.869 ms | 1 - 202 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 26.065 ms | 1 - 281 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 20.489 ms | 0 - 355 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 33.281 ms | 0 - 432 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 272.269 ms | 0 - 329 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 44.565 ms | 0 - 3 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 72.513 ms | 0 - 330 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 78.557 ms | 0 - 71 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 83.942 ms | 0 - 316 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 272.269 ms | 0 - 329 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 77.812 ms | 0 - 246 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.329 ms | 0 - 349 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.145 ms | 0 - 247 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.09 ms | 0 - 270 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 94.653 ms | 0 - 39 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 38.256 ms | 0 - 206 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.359 ms | 0 - 5 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8775P | 14.754 ms | 0 - 205 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 14.952 ms | 0 - 35 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCM6690 | 433.127 ms | 0 - 352 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 23.27 ms | 0 - 268 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA7255P | 38.256 ms | 0 - 206 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8295P | 21.338 ms | 0 - 209 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.553 ms | 0 - 199 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 26.346 ms | 0 - 281 MB | NPU

## License
* The license for the original implementation of FCN-ResNet50 can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1411.4038)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/fcn.py)

## 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).