Yolo-v7

    Real-time object detection optimized for mobile and edge.

    YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image.

    Qualcomm® QCS8550
    QCS8550 (Proxy)
    TorchScriptTFLite
    20.9ms
    Inference Time
    9-12MB
    Memory Usage
    292NPU
    21CPU
    Layers

    Technical Details

    Model checkpoint:YoloV7 Tiny
    Input resolution:720p (720x1280)
    Number of parameters:6.39M
    Model size:24.4 MB

    Applicable Scenarios

    • Factory Automation
    • Robotic Navigation
    • Camera

    Licenses

    Source Model:GPL-3.0
    Deployable Model:GPL-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