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---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: image-segmentation
---
# YOLOv11-Segmentation: Optimized for Qualcomm Devices
Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
This is based on the implementation of YOLOv11-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) 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
Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) 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
See our repository for [YOLOv11-Segmentation on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov11_seg) for usage instructions.
## Model Details
**Model Type:** Model_use_case.semantic_segmentation
**Model Stats:**
- Model checkpoint: YOLO11N-Seg
- Input resolution: 640x640
- Number of output classes: 80
- Number of parameters: 2.89M
- Model size (float): 11.1 MB
- Model size (w8a16): 11.4 MB
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| YOLOv11-Segmentation | ONNX | float | Snapdragon® X Elite | 6.416 ms | 17 - 17 MB | NPU
| YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.233 ms | 2 - 206 MB | NPU
| YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 6.171 ms | 11 - 15 MB | NPU
| YOLOv11-Segmentation | ONNX | float | Qualcomm® QCS9075 | 8.053 ms | 11 - 14 MB | NPU
| YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.491 ms | 1 - 160 MB | NPU
| YOLOv11-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.043 ms | 0 - 155 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 3.147 ms | 0 - 171 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 15.466 ms | 4 - 111 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4.342 ms | 4 - 9 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8775P | 6.082 ms | 4 - 113 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 5.829 ms | 4 - 22 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 10.084 ms | 4 - 210 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA7255P | 15.466 ms | 4 - 111 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Qualcomm® SA8295P | 9.237 ms | 4 - 177 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.39 ms | 0 - 116 MB | NPU
| YOLOv11-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.957 ms | 0 - 121 MB | NPU
## License
* The license for the original implementation of YOLOv11-Segmentation can be found
[here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
## References
* [Ultralytics YOLOv11 Docs: Instance Segmentation](https://docs.ultralytics.com/tasks/segment/)
* [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment)
## 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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