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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.

Technical Details

Model checkpoint:unet_carvana_scale1.0_epoch2
Input resolution:224x224
Number of parameters:31.0M
Model size:118 MB
Number of output classes:2 (foreground / background)

Applicable Scenarios

  • Autonomous Vehicles
  • Medical Imaging
  • Factory Quality Control

Supported Form Factors

  • Phone
  • Tablet
  • IoT
  • XR

Licenses

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

Tags

  • backbone
  • real-time

Supported Devices

  • QCS8550 (Proxy)
  • SA8255 (Proxy)
  • SA8295P ADP
  • SA8650 (Proxy)
  • SA8775 (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
  • Snapdragon 8 Elite QRD
  • Snapdragon X Elite CRD
  • Snapdragon X Plus 8-Core CRD
  • Xiaomi 12
  • Xiaomi 12 Pro

Supported Chipsets

  • Qualcomm® QCS8550 (Proxy)
  • Qualcomm® SA8255P (Proxy)
  • Qualcomm® SA8295P
  • Qualcomm® SA8650P (Proxy)
  • Qualcomm® SA8775P (Proxy)
  • Snapdragon® 8 Elite Mobile
  • Snapdragon® 8 Gen 1 Mobile
  • Snapdragon® 8 Gen 2 Mobile
  • Snapdragon® 8 Gen 3 Mobile
  • Snapdragon® 888 Mobile
  • Snapdragon® X Elite
  • Snapdragon® X Plus 8-Core

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