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
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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