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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. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana‑image‑masking‑challenge) and is used for vehicle segmentation.

Not supported

This model is currently not supported on any IoT chipset.

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Technical Details

Input resolution:640x1280
Model checkpoint:unet_carvana_scale1.0_epoch2
Model size (float):118 MB
Model size (w8a8):29.8 MB
Number of output classes:2 (foreground / background)
Number of parameters:31.0M

Applicable Scenarios

  • Autonomous Vehicles
  • Medical Imaging
  • Factory Quality Control

License

Model:GPL-3.0

Tags

  • backbone
  • real-time

Supported IoT Devices

  • Arduino VENTUNO Q
  • Dragonwing IQ-8275 EVK
  • Dragonwing IQ-9075 EVK
  • Dragonwing IQ-X5121
  • Dragonwing IQ-X7181
  • Dragonwing Q-6690 MTP
  • Dragonwing Q-7790
  • Dragonwing Q-8750
  • Dragonwing RB3 Gen 2 Vision Kit
  • QCS8550 (Proxy)

Supported IoT Chipsets

  • Qualcomm® Dragonwing™ Q-6690
  • Qualcomm® QCS5121
  • Qualcomm® Dragonwing™ QCS6490
  • Qualcomm® Dragonwing™ IQ-X7181
  • Qualcomm® Dragonwing™ Q-7790
  • Qualcomm® Dragonwing™ IQ-8275
  • Qualcomm® Dragonwing™ QCS8550 (Proxy)
  • Qualcomm® Dragonwing™ Q-8750
  • Qualcomm® Dragonwing™ IQ-9075

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