--- library_name: pytorch license: agpl-3.0 tags: - real_time - android pipeline_tag: object-detection --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov11_det/web-assets/model_demo.png) # YOLOv11-Detection: Optimized for Mobile Deployment ## Real-time object detection optimized for mobile and edge by Ultralytics Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is an implementation of YOLOv11-Detection found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect). More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov11_det). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Model checkpoint: YOLO11-N - Input resolution: 640x640 - Number of parameters: None - Model size: None | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | YOLOv11-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.538 ms | 0 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.354 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv11-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.001 ms | 5 - 32 MB | FP16 | NPU | -- | | YOLOv11-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 5.43 ms | 0 - 48 MB | FP16 | NPU | -- | | YOLOv11-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.051 ms | 5 - 26 MB | FP16 | NPU | -- | | YOLOv11-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.687 ms | 5 - 62 MB | FP16 | NPU | -- | | YOLOv11-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.203 ms | 0 - 42 MB | FP16 | NPU | -- | | YOLOv11-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.26 ms | 5 - 47 MB | FP16 | NPU | -- | | YOLOv11-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.23 ms | 5 - 52 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA7255P ADP | SA7255P | TFLITE | 60.993 ms | 0 - 38 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA7255P ADP | SA7255P | QNN | 56.875 ms | 0 - 8 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 7.583 ms | 0 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.354 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8295P ADP | SA8295P | TFLITE | 11.969 ms | 0 - 26 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8295P ADP | SA8295P | QNN | 8.391 ms | 0 - 14 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 7.589 ms | 0 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.407 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8775P ADP | SA8775P | TFLITE | 10.563 ms | 0 - 38 MB | FP16 | NPU | -- | | YOLOv11-Detection | SA8775P ADP | SA8775P | QNN | 6.712 ms | 1 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 60.993 ms | 0 - 38 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 56.875 ms | 0 - 8 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.532 ms | 0 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.359 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 10.563 ms | 0 - 38 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 6.712 ms | 1 - 11 MB | FP16 | NPU | -- | | YOLOv11-Detection | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 10.624 ms | 0 - 34 MB | FP16 | NPU | -- | | YOLOv11-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 4.848 ms | 5 - 5 MB | FP16 | NPU | -- | | YOLOv11-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.622 ms | 5 - 5 MB | FP16 | NPU | -- | ## License * The license for the original implementation of YOLOv11-Detection 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: Object Detection](https://docs.ultralytics.com/tasks/detect/) * [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect) ## 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