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
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 modelsLooking for more? See models created by industry leaders.
Discover Model Makers