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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.

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
Number of output classes:19

Applicable Scenarios

  • Self-driving cars

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 Compute Chipsets

  • Snapdragon® X Elite