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.

🏁 ELICE 1st Team Project

🏁 ELICE 1st Team Project

[Project&Competition] 2023 장애인 해커톤 대회 - 시각장애인을 위한 보행 방향 및 길 안내 서비스 제안

Repository for semantic segmentation of aerial imagery using U-Net, featuring training scripts, data preprocessing, and model evaluation.

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.

This is a warehouse for DeepLabV3-Xception-pytorch-model, can be used to train your segmentation datasets

NYU Deep Learning 2023 project for semantic segmentation in video sequences using dual-phase learning with U-Net and ConvLSTM, focusing on predicting segmentation masks from synthetic 3D shape videos.

Official implementation of DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (pytorch implementation)

Classification and segmentation 4 VietNamese foods in using Pytorch

An attention-based solution on long-tail 3D point cloud semantic segmentation tasks.

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

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.

pre trained deeplabV3 with different backbones

Deep learning based semantic segmentation Using the FCN.

road and traffic segmentation with IoU metric and DICE coffecient