HomeIoT ModelsYOLOv8-Segmentation

    YOLOv8-Segmentation

    Real-time object segmentation optimized for mobile and edge by Ultralytics.

    Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.

    Qualcomm® QCS8550
    QCS8550 (Proxy)
    TorchScriptTFLite
    7.22ms
    Inference Time
    4-17MB
    Memory Usage
    337NPU
    Layers

    Technical Details

    Model checkpoint:YOLOv8N-Seg
    Input resolution:640x640
    Number of parameters:3.43M
    Model size:13.2 MB

    Applicable Scenarios

    • Factory Automation
    • Robotic Navigation
    • Camera

    Licenses

    Source Model:AGPL-3.0
    Deployable Model:AGPL-3.0

    Tags

    • real-time
      A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.

    Supported IoT Devices

    • QCS8550 (Proxy)

    Supported IoT Chipsets

    • Qualcomm® QCS8550