HomeMobile ModelsUnet-Segmentation

    Unet-Segmentation

    Real-time segmentation optimized for mobile and edge.

    UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel.

    113ms
    Inference Time
    5-343MB
    Memory Usage
    31NPU
    Layers

    Technical Details

    Model checkpoint:unet_carvana_scale1.0_epoch2
    Input resolution:224x224
    Number of parameters:31.0M
    Model size:118 MB

    Applicable Scenarios

    • Autonomous Vehicles
    • Medical Imaging
    • Factory Quality Control

    Supported Mobile Form Factors

    • Phone
    • Tablet

    Licenses

    Source Model:GPL-3.0
    Deployable Model:GPL-3.0

    Tags

    • backbone
      A “backbone” model is designed to extract task-agnostic representations from specific data modalities (e.g., images, text, speech). This representation can then be fine-tuned for specialized tasks.
    • 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