HomeMobile ModelsFFNet-78S-Quantized

    FFNet-78S-Quantized

    Semantic segmentation for automotive street scenes.

    FFNet-78S-Quantized is a "fuss-free network" that segments street scene images with per-pixel classes like road, sidewalk, and pedestrian. Trained on the Cityscapes dataset.

    TorchScriptTFLite
    5.99ms
    Inference Time
    0-83MB
    Memory Usage
    154NPU
    Layers

    Technical Details

    Model checkpoint:ffnet78S_dBBB_cityscapes_state_dict_quarts
    Input resolution:2048x1024
    Number of parameters:27.5M
    Model size:26.7 MB

    Applicable Scenarios

    • Automotive
    • Autonomous Driving
    • Camera

    Supported Mobile Form Factors

    • Phone
    • Tablet

    Licenses

    Source Model:BSD-3-CLAUSE
    Deployable Model:AI Model Hub License

    Tags

    • quantized
      A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.
    • 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