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.
More details on model performance across various devices, can be found here.
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 | 8.653 ms | 4 - 23 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 115.357 ms | 95 - 104 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 6.283 ms | 0 - 48 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 98.029 ms | 99 - 125 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.026 ms | 3 - 47 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 73.514 ms | 108 - 123 MB | FP32 | CPU | -- |
| YOLOv11-Segmentation | SA7255P ADP | SA7255P | TFLITE | 81.898 ms | 4 - 45 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 8.781 ms | 4 - 24 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8295P ADP | SA8295P | TFLITE | 13.37 ms | 4 - 30 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 8.581 ms | 4 - 25 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | SA8775P ADP | SA8775P | TFLITE | 12.205 ms | 4 - 44 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 81.898 ms | 4 - 45 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 8.651 ms | 4 - 25 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 12.205 ms | 4 - 44 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 12.372 ms | 4 - 44 MB | FP16 | NPU | -- |
| YOLOv11-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 32.566 ms | 116 - 116 MB | FP32 | CPU | -- |
License
- The license for the original implementation of YOLOv11-Segmentation can be found here.
- The license for the compiled assets for on-device deployment can be found here
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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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