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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.

Snapdragon® X Elite
146ms
Inference Time
17MB
Memory Usage
53NPU
Layers

Technical Details

Model checkpoint:unet_carvana_scale1.0_epoch2
Input resolution:224x224
Number of parameters:31.0M
Model size:118 MB

Applicable Scenarios

  • Autonomous Vehicles
  • Medical Imaging
  • Factory Quality Control

Licenses

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

Tags

  • backbone
    A “backbone” model is designed to extract task-agnostic representations from specific data modalities (e.g., images, text, speech). This representation can then be fine-tuned for specialized tasks.
  • 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