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    DDRNet23-Slim

    Segment images or video by class in real-time on device.

    DDRNet23Slim is a machine learning model that segments an image into semantic classes, specifically designed for road-based scenes. It is designed for the application of self-driving cars.

    TorchScriptTFLite
    4.57ms
    Inference Time
    0-68MB
    Memory Usage
    131NPU
    Layers
    See more metrics
    Model RepositoryHugging FaceResearch Paper

    Technical Details

    Model checkpoint:DDRNet23s_imagenet.pth
    Inference latency:RealTime
    Input resolution:2048x1024
    Number of parameters:5.69M
    Model size:21.7 MB

    Applicable Scenarios

    • Self-driving cars

    Supported Mobile Form Factors

    • Phone
    • Tablet

    Licenses

    Source Model:MIT
    Deployable Model:AI Model Hub License

    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 Mobile Devices

    • Google Pixel 3
    • Google Pixel 3a
    • Google Pixel 3a XL
    • Google Pixel 4
    • Google Pixel 4a
    • Google Pixel 5a 5G
    • Samsung Galaxy S21
    • Samsung Galaxy S21 Ultra
    • Samsung Galaxy S21+
    • Samsung Galaxy S22 5G
    • Samsung Galaxy S22 Ultra 5G
    • Samsung Galaxy S22+ 5G
    • Samsung Galaxy S23
    • Samsung Galaxy S23 Ultra
    • Samsung Galaxy S23+
    • Samsung Galaxy S24
    • Samsung Galaxy S24 Ultra
    • Samsung Galaxy S24+
    • Samsung Galaxy Tab S8
    • Xiaomi 12
    • Xiaomi 12 Pro

    Supported Mobile Chipsets

    • Snapdragon® 8 Gen 1 Mobile
    • Snapdragon® 8 Gen 2 Mobile
    • Snapdragon® 8 Gen 3 Mobile
    • Snapdragon® 888 Mobile