--- 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/yolov8_det/web-assets/model_demo.png) # YOLOv8-Detection: Optimized for Mobile Deployment ## Real-time object detection optimized for mobile and edge by Ultralytics Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is an implementation of YOLOv8-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/yolov8_det). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Model checkpoint: YOLOv8-N - Input resolution: 640x640 - Number of parameters: 3.18M - Model size: 12.2 MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | YOLOv8-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 5.164 ms | 0 - 17 MB | FP16 | NPU | -- | | YOLOv8-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 5.053 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 6.183 ms | 5 - 39 MB | FP16 | NPU | -- | | YOLOv8-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 3.706 ms | 0 - 47 MB | FP16 | NPU | -- | | YOLOv8-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.46 ms | 5 - 20 MB | FP16 | NPU | -- | | YOLOv8-Detection | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.371 ms | 5 - 64 MB | FP16 | NPU | -- | | YOLOv8-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.686 ms | 0 - 44 MB | FP16 | NPU | -- | | YOLOv8-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.635 ms | 5 - 56 MB | FP16 | NPU | -- | | YOLOv8-Detection | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 3.347 ms | 5 - 57 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA7255P ADP | SA7255P | TFLITE | 71.655 ms | 0 - 35 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA7255P ADP | SA7255P | QNN | 70.864 ms | 1 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 5.167 ms | 0 - 16 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8255 (Proxy) | SA8255P Proxy | QNN | 5.013 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8295P ADP | SA8295P | TFLITE | 9.939 ms | 0 - 28 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 5.173 ms | 0 - 19 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.998 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8775P ADP | SA8775P | TFLITE | 8.129 ms | 0 - 35 MB | FP16 | NPU | -- | | YOLOv8-Detection | SA8775P ADP | SA8775P | QNN | 7.974 ms | 0 - 8 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 71.655 ms | 0 - 35 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 70.864 ms | 1 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 5.145 ms | 0 - 17 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 5.0 ms | 5 - 7 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 8.129 ms | 0 - 35 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 7.974 ms | 0 - 8 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 8.597 ms | 0 - 31 MB | FP16 | NPU | -- | | YOLOv8-Detection | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 7.603 ms | 5 - 40 MB | FP16 | NPU | -- | | YOLOv8-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 5.419 ms | 5 - 5 MB | FP16 | NPU | -- | | YOLOv8-Detection | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.696 ms | 5 - 5 MB | FP16 | NPU | -- | ## License * The license for the original implementation of YOLOv8-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 YOLOv8 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