Official implementation of DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (pytorch implementation)
State-of-the-Art Deep Learning Models in TensorFlow Modern Machine Learning in the Google Colab Ecosystem
Image segmentation task with KiTS19 challenge data using U-net
Classification and segmentation 4 VietNamese foods in using Pytorch
Unveiling the secrets of an ancient library buried by Mount Vesuvius, this Kaggle competition, supported by the Vesuvius Challenge organization, tasked participants with detecting ink from 3D X-ray scans of charred scrolls preserved in a Roman villa in Herculaneum.
Deep learning pipeline that transforms 2D floor plan images into 3D architectural models using semantic segmentation and morphological processing
Deep learning based semantic segmentation Using the FCN.
[Project&Competition] 2023 장애인 해커톤 대회 - 시각장애인을 위한 보행 방향 및 길 안내 서비스 제안
road and traffic segmentation with IoU metric and DICE coffecient
Semantic segmentation is a type of computer vision technique that assigns a label to every pixel in an image. The label indicates the class of object that the pixel represents. Semantic segmentation is used in tasks such as self-driving cars, where it is important to know not only the boundaries of objects, but also what those objects are.
Official PyTorch implementation of "A Class-Aware Semi-Supervised Framework for Semantic Segmentation of High-Resolution Remote Sensing Imagery" (IEEE JSTARS 2025)
🏁 ELICE 1st Team Project
Repository for semantic segmentation of aerial imagery using U-Net, featuring training scripts, data preprocessing, and model evaluation.
🏁 ELICE 1st Team Project
An attention-based solution on long-tail 3D point cloud semantic segmentation tasks.
Colorizes thermal-infrared satellite images (Landsat 8/9) with a U-Net and checks that land-cover meaning is preserved.
Self-supervised MAE-ViT pipeline for label-efficient semantic segmentation on ADE20K and Cityscapes.
This is a warehouse for DeepLabV3-Xception-pytorch-model, can be used to train your segmentation datasets
A course exercise implementing a pre-trained U-Net model for semantic segmentation of satellite imagery, featuring data preprocessing, augmentation, model training, and performance evaluation with visualization scripts.
U-Net++ trained from scratch for 7-class segmentation of mechanical parts, with encoder ensembling and test-time augmentation
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