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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.

4.66ms
Inference Time
0-71MB
Memory Usage
131NPU
Layers

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 Form Factors

  • Phone
  • Tablet
  • IoT

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 Devices

  • Google Pixel 3
  • Google Pixel 3a
  • Google Pixel 3a XL
  • Google Pixel 4
  • Google Pixel 4a
  • Google Pixel 5a 5G
  • QCS8550 (Proxy)
  • 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 Chipsets

  • Qualcomm® QCS8550
  • Snapdragon® 8 Gen 1 Mobile
  • Snapdragon® 8 Gen 2 Mobile
  • Snapdragon® 8 Gen 3 Mobile
  • Snapdragon® 888 Mobile
  • Snapdragon® X Elite