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: Model_use_case.semantic_segmentation
- Model Stats:
- Model checkpoint: YOLO11N-Seg
- Input resolution: 640x640
- Number of parameters: 2.9M
- Number of output classes: 80
- Model size (float): 11.1 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| YOLOv11-Segmentation | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 20.824 ms | 4 - 44 MB | NPU | -- |
| YOLOv11-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 12.33 ms | 4 - 44 MB | NPU | -- |
| YOLOv11-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 8.589 ms | 4 - 22 MB | NPU | -- |
| YOLOv11-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 10.507 ms | 4 - 45 MB | NPU | -- |
| YOLOv11-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 20.824 ms | 4 - 44 MB | NPU | -- |
| YOLOv11-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 8.517 ms | 6 - 24 MB | NPU | -- |
| YOLOv11-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 13.142 ms | 4 - 31 MB | NPU | -- |
| YOLOv11-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 8.537 ms | 4 - 23 MB | NPU | -- |
| YOLOv11-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 10.507 ms | 4 - 45 MB | NPU | -- |
| YOLOv11-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 8.646 ms | 4 - 23 MB | NPU | -- |
| YOLOv11-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 117.089 ms | 84 - 99 MB | CPU | -- |
| YOLOv11-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 6.174 ms | 4 - 54 MB | NPU | -- |
| YOLOv11-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 96.033 ms | 98 - 126 MB | CPU | -- |
| YOLOv11-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 5.086 ms | 4 - 47 MB | NPU | -- |
| YOLOv11-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 89.042 ms | 68 - 83 MB | CPU | -- |
| YOLOv11-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 35.314 ms | 114 - 114 MB | CPU | -- |
| YOLOv11-Segmentation | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 162.642 ms | 143 - 149 MB | CPU | -- |
| YOLOv11-Segmentation | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 128.362 ms | 156 - 181 MB | CPU | -- |
| YOLOv11-Segmentation | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 112.747 ms | 129 - 146 MB | CPU | -- |
| YOLOv11-Segmentation | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 60.715 ms | 258 - 258 MB | 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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