The synergy of UNet, Swin Transformer, and CNN is not purely academic; it offers tangible benefits across various fields: In medical diagnostics, precision and detail are paramount. This hybrid ...

To address these challenges, we develop a standard multimodal coating defect dataset (MCD-AD) and propose a multimodal unsupervised coating defect detection method based on dual-branch hybrid ...

Traditional segmentation networks like UNet face difficulties in capturing comprehensive long-range dependencies within the feature space due to the limitations of CNN receptive fields. This ...

The system uses deep learning models (U-Net, ResNet-UNet, and CNN-RNN hybrid architectures) to identify text regions in document images before performing OCR.

Comparison of transfer learning on Meta’s MaskFormer, a ViT-based semantic segmentation model, against generic UNet Convolutional Neural networks ... weighted loss function significantly boosts the ...

This work proposes a effective CNN based on 3DUnet, CAAL-Unet. It uses 3D partical Convolution to replace the traditional convolution, thereby reducing network parameters and redundant feature maps.

The second file is the jupyter notebook Deep_Learning_UNet.ipynb. This notebook details the U-Net's configuration and also contains the training loop and test statistics generation. This file has been ...