U2Net-Segmentation
Salient object segmentation for high‑quality foreground extraction.
U2‑Net is a machine learning model for salient object detection and foreground extraction. It uses a two‑level nested U‑structure (RSU blocks) to capture both local and global context at multiple scales, producing high‑quality binary foreground masks. The model is trained on the DUTS dataset and is well‑suited for background removal, portrait segmentation, and object extraction on mobile and edge devices.
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
Input resolution:320x320
Model checkpoint:u2net.pth
Number of output classes:1 (salient foreground mask)
Number of parameters:44.0M
Applicable Scenarios
- Background Removal
- Portrait Segmentation
- E-Commerce Product Imaging
License
Model:APACHE-2.0
Tags
- real-time
Supported IoT Devices
- Arduino VENTUNO Q
- 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
- QCS8550 (Proxy)
Supported IoT Chipsets
- Qualcomm® Dragonwing™ Q-6690
- Qualcomm® QCS5121
- Qualcomm® Dragonwing™ QCS6490
- Qualcomm® Dragonwing™ IQ-X7181
- Qualcomm® Dragonwing™ Q-7790
- Qualcomm® Dragonwing™ IQ-8275
- Qualcomm® Dragonwing™ QCS8550 (Proxy)
- Qualcomm® Dragonwing™ Q-8750
- Qualcomm® Dragonwing™ IQ-9075
Related Models
See all modelsLooking for more? See models created by industry leaders.
Discover Model Makers









