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---
library_name: pytorch
license: agpl-3.0
tags:
- real_time
- android
pipeline_tag: image-segmentation
---
# YOLOv11-Segmentation: Optimized for Mobile Deployment
## Real-time object segmentation optimized for mobile and edge by Ultralytics
Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
This model is an implementation of YOLOv11-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov11_seg).
### Model Details
- **Model Type:** Semantic segmentation
- **Model Stats:**
- Model checkpoint: YOLO11N-Seg
- Input resolution: 640x640
- Number of parameters: 2.9M
- Model size: 11.1 MB
- Number of output classes: 80
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YOLOv11-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 8.548 ms | 4 - 24 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 90.377 ms | 94 - 103 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 6.225 ms | 4 - 53 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 78.39 ms | 100 - 127 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.028 ms | 4 - 49 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 88.86 ms | 104 - 118 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | SA7255P ADP | SA7255P | TFLITE | 81.882 ms | 4 - 43 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 8.527 ms | 4 - 24 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8295P ADP | SA8295P | TFLITE | 13.348 ms | 4 - 30 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 8.571 ms | 4 - 25 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8775P ADP | SA8775P | TFLITE | 12.198 ms | 4 - 44 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 81.882 ms | 4 - 43 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 8.499 ms | 4 - 23 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 12.198 ms | 4 - 44 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 12.136 ms | 4 - 48 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 32.266 ms | 117 - 117 MB | FP32 | CPU | -- |
## License
* The license for the original implementation of YOLOv11-Segmentation can be found
[here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment 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://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) 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).
## Usage and Limitations
Model may not be used for or in connection with any of the following applications:
- Accessing essential private and public services and benefits;
- Administration of justice and democratic processes;
- Assessing or recognizing the emotional state of a person;
- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
- Education and vocational training;
- Employment and workers management;
- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
- General purpose social scoring;
- Law enforcement;
- Management and operation of critical infrastructure;
- Migration, asylum and border control management;
- Predictive policing;
- Real-time remote biometric identification in public spaces;
- Recommender systems of social media platforms;
- Scraping of facial images (from the internet or otherwise); and/or
- Subliminal manipulation
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