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.
Not supported
This model is currently not supported on any IoT chipset.
To see performance metrics for this model on other chipsets, click the button below.
View for other chipsetsTechnical Details
Model checkpoint:unet_carvana_scale1.0_epoch2
Input resolution:640x1280
Number of output classes:2 (foreground / background)
Number of parameters:31.0M
Model size (float):118 MB
Model size (w8a8):29.8 MB
Applicable Scenarios
- Autonomous Vehicles
- Medical Imaging
- Factory Quality Control
License
Model:GPL-3.0
Tags
- backbone
- real-time
Supported IoT Devices
- Dragonwing IQ-9075 EVK
- Dragonwing IQ-X5121
- Dragonwing IQ-X7181
- Dragonwing Q-6690 MTP
- Dragonwing Q-7790
- Dragonwing Q-8750
- Dragonwing RB3 Gen 2 Vision Kit
- QCS8275 (Proxy)
- QCS8550 (Proxy)
Supported IoT Chipsets
- Qualcomm® QCM6690
- Qualcomm® QCS6490
- Qualcomm® QCS8275 (Proxy)
- Qualcomm® QCS8550 (Proxy)
- Qualcomm® QCS9075
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