semantic segmentation

This is a project to train, use and analyze 2D and 3D neural networks for segmentation.

[SOICT2023] Official Implementation of MCLDA: Multi-level Contrastive Learning for Domain Adaptive Semantic Segmentation

U-Net Adaptive Generalized Image Binarization for Documents

Mirrored from: https://github.com/venkatakolagotla/robin Originally forked from: https://github.com/masyagin1998/robin

This repository contains all my work related to the study of effectiveness of wavelet feature extraction on: Pose estimation Human segmentation Object detection Image Processing

This is a project to train, use and analyze 2D and 3D neural networks for segmentation. It contains a UI and is implemented in pytorch and django as backend.

Dense Prediction by Means of Self-attention Layers - research of models for dense prediction (semantic segmentation) - primarily transformers (models in focus: Segmenter, Swin transformer) and comparison with convolutional models (model in focus: pyramidal SwiftNet). Also, research, design and implementation of pyramidal models of transformer-convolutional model (Segmenter-SwiftNet) and transformer-transformer (Segmenter-Segmenter) type. Implementation is in PyTorch deep learning framework. My graduate thesis computer vision project.