--- library_name: pytorch license: other tags: - real_time - android pipeline_tag: image-segmentation --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov11_seg/web-assets/model_demo.png) # 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:** 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 | 20.834 ms | 4 - 44 MB | NPU | -- | | YOLOv11-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 12.372 ms | 4 - 41 MB | NPU | -- | | YOLOv11-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 8.485 ms | 4 - 24 MB | NPU | -- | | YOLOv11-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 10.485 ms | 4 - 44 MB | NPU | -- | | YOLOv11-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 20.834 ms | 4 - 44 MB | NPU | -- | | YOLOv11-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 8.453 ms | 4 - 23 MB | NPU | -- | | YOLOv11-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 13.209 ms | 4 - 30 MB | NPU | -- | | YOLOv11-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 8.411 ms | 4 - 22 MB | NPU | -- | | YOLOv11-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 10.485 ms | 4 - 44 MB | NPU | -- | | YOLOv11-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 8.487 ms | 4 - 23 MB | NPU | -- | | YOLOv11-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 116.27 ms | 98 - 107 MB | CPU | -- | | YOLOv11-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 6.195 ms | 4 - 50 MB | NPU | -- | | YOLOv11-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 82.809 ms | 99 - 127 MB | CPU | -- | | YOLOv11-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 5.907 ms | 4 - 48 MB | NPU | -- | | YOLOv11-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 88.977 ms | 68 - 83 MB | CPU | -- | | YOLOv11-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 35.32 ms | 114 - 114 MB | CPU | -- | | YOLOv11-Segmentation | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 135.626 ms | 153 - 159 MB | CPU | -- | | YOLOv11-Segmentation | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 116.692 ms | 164 - 189 MB | CPU | -- | | YOLOv11-Segmentation | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 112.873 ms | 131 - 148 MB | CPU | -- | | YOLOv11-Segmentation | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 56.537 ms | 258 - 258 MB | 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