---
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/v0.52.0/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/v0.52.0/fcn_resnet50-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.52.0/fcn_resnet50-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.52.0/fcn_resnet50-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.52.0/fcn_resnet50-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fcn_resnet50/releases/v0.52.0/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 | 22.469 ms | 4 - 325 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® X2 Elite | 23.298 ms | 63 - 63 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® X Elite | 43.083 ms | 62 - 62 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 32.662 ms | 0 - 383 MB | NPU
| FCN-ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 43.797 ms | 3 - 6 MB | NPU
| FCN-ResNet50 | ONNX | float | Qualcomm® QCS9075 | 75.188 ms | 3 - 9 MB | NPU
| FCN-ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.75 ms | 1 - 307 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.064 ms | 1 - 249 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X2 Elite | 7.103 ms | 33 - 33 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® X Elite | 13.847 ms | 32 - 32 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 10.021 ms | 0 - 277 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS6490 | 911.428 ms | 68 - 111 MB | CPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 13.52 ms | 1 - 4 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCS9075 | 15.133 ms | 1 - 4 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Qualcomm® QCM6690 | 824.523 ms | 64 - 73 MB | CPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.569 ms | 1 - 206 MB | NPU
| FCN-ResNet50 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 705.859 ms | 67 - 75 MB | CPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 21.54 ms | 3 - 332 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X2 Elite | 22.917 ms | 3 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 44.587 ms | 3 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 33.413 ms | 0 - 378 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 273.555 ms | 2 - 308 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 44.055 ms | 3 - 6 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 72.905 ms | 1 - 308 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 78.314 ms | 3 - 8 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 84.09 ms | 0 - 269 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 273.555 ms | 2 - 308 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 77.805 ms | 2 - 219 MB | NPU
| FCN-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 26.556 ms | 3 - 321 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.425 ms | 1 - 245 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 7.836 ms | 1 - 1 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® X Elite | 15.141 ms | 1 - 1 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 11.262 ms | 0 - 263 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 93.356 ms | 1 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 39.438 ms | 1 - 207 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.835 ms | 1 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 14.725 ms | 1 - 216 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 17.237 ms | 1 - 3 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 397.792 ms | 1 - 384 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 24.453 ms | 1 - 264 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 39.438 ms | 1 - 207 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 21.911 ms | 1 - 210 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.955 ms | 1 - 200 MB | NPU
| FCN-ResNet50 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 26.284 ms | 1 - 284 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 20.875 ms | 0 - 368 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 33.054 ms | 0 - 428 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 273.485 ms | 0 - 353 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 44.407 ms | 0 - 3 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 72.835 ms | 0 - 354 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 79.293 ms | 0 - 71 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 83.983 ms | 0 - 323 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 273.485 ms | 0 - 353 MB | NPU
| FCN-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 77.795 ms | 0 - 268 MB | NPU
| FCN-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.156 ms | 0 - 358 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.358 ms | 0 - 244 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 10.954 ms | 0 - 264 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 95.436 ms | 0 - 39 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 38.208 ms | 0 - 205 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.241 ms | 0 - 3 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8775P | 14.685 ms | 0 - 207 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 15.381 ms | 0 - 35 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCM6690 | 436.069 ms | 0 - 382 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 23.266 ms | 0 - 265 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA7255P | 38.208 ms | 0 - 205 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Qualcomm® SA8295P | 21.286 ms | 0 - 210 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 8.599 ms | 0 - 198 MB | NPU
| FCN-ResNet50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 27.95 ms | 0 - 284 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).