Qualcomm® AI HubAI Hub

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

Licenses

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

Tags

  • backbone
  • real-time

Supported Automotive Devices

  • SA7255P ADP
  • SA8255 (Proxy)
  • SA8295P ADP
  • SA8650 (Proxy)
  • SA8775P ADP

Supported Automotive Chipsets

  • Qualcomm® SA7255P
  • Qualcomm® SA8255P (Proxy)
  • Qualcomm® SA8295P
  • Qualcomm® SA8650P (Proxy)
  • Qualcomm® SA8775P

Related Models

See all models

Looking for more? See models created by industry leaders.

Discover Model Makers