#U-net
Showing 16 of 16 repositories tagged #u-net, ranked by stars
《深度学习与计算机视觉》配套代码
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Real-Time Semantic Segmentation in Mobile device
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Implementation of a U-net complete with efficient attention as well as the latest research findings
Manage your machine learning experiments with trixi - modular, reproducible, high fashion. An experiment infrastructure optimized for PyTorch, but flexible enough to work for your framework and your tastes.
A Pytorch implementation of Stylegan2 with UNet Discriminator
Tensorflow implementation : U-net and FCN with global convolution
Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data
DATA-SCIENCE-BOWL-2018 Find the nuclei in divergent images to advance medical discovery
Boost segmentation model mIoU/Dice instantly WITHOUT retraining. A plug-and-play, training-free optimization module. Published in NeurIPS & JMLR. Compatible with SAM, DeepLab, SegFormer, and more. 🧩
pytorch implementation of paper https://www.frontiersin.org/articles/10.3389/fcomp.2020.00035/full
We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. We used UNET model for our segmentation.
Deep learning grayscale (black and white) to color image conversion using U-Net autoencoder architecture in PyTorch. Converts grayscale images to RGB using LAB color space prediction with encoder-decoder neural networks.
Promethium is a state-of-the-art, AI-driven framework for seismic signal reconstruction, denoising, and geophysical data enhancement, integrating cutting-edge deep learning architectures with production-grade data engineering.