segmentation-models-pytorch

🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).

3クラス(肌、服、髪)のセマンティックセグメンテーションを実施するモデル(A model that performs semantic segmentation of 3 classes(skin, clothes, hair))

Semantic Segmentation on Cityscapes using Segmentation Models Pytorch

A full deep learning pipeline for satellite image semantic segmentation (originally designed for detection of built structures) using Segmentation Models PyTorch.

Segmentation of cancerous tumors using Mamba. Code, resources, and paper provided. We manage to make a small (42k param) model that can segment pretty well.

This repository contains the code to train a custom DGCNN segmentation model on 3D point cloud data and carry out post-processing to filter these point clouds from the k-regular graphs produced by the model.

Segmenting the functional tissue units of colon histopathology images.

Bone Age Maturity estimation using a Lateral Cephalogram X-ray image and deep neural networks (UNet)

Implementing FCN & U-Net from scratch, then scaling with segmentation_models_pytorch — covering binary and multi-class segmentation on real-world datasets. / FCN, U-Net 논문 직접 구현부터 SMP 라이브러리 활용까지 — 실제 데이터셋으로 이진/다중 클래스 세그멘테이션을 단계적으로 학습한 기록.

This repository contains code and results for a semantic segmentation competition using a small autonomous driving dataset. The project explores various data augmentation, network architectures, and training strategies to improve segmentation accuracy and efficiency.

Multi-class brain tumor segmentation from MRI using 3D Attention U-Net

A config-first toolkit for semantic segmentation training, evaluation, and image/video inference.

The application is designed for editing a car: changing the color of the body, lights, windows and wheels

Deep Hierarchical Semantic Segmentation — Pixel-Wise Classification with Multi-Level Labels

PyTorch-based human image segmentation pipeline using a U-Net model with an EfficientNet-B0 encoder, segmentation-safe augmentations, and binary mask prediction.

Code accompanying the paper "Comprehensive evaluation of cross-cancer generalization in histopathology segmentation models across 21 tumor types." Includes annotation scripts, segmentation inference, manual scoring app, and statistical analysis.

DeepLabV3+ semantic segmentation on Pascal VOC 2012 with reproducible PyTorch training, holdout evaluation, FastAPI inference, and Streamlit visualization.

This project offers an easy, flexible, modular PyTorch implementation for semantic segmentation to minimize configuration, automate training and deployment, and enable customization of models, encoders, losses and datasets through its modular design.

Deep learning system for automatic brain tumor segmentation from MRI images using U-Net with ResNet50 encoder, achieving high Dice Score and IoU for accurate medical image analysis.

Multidataset wall-crack segmentation research comparing U-Net, DeepLabV3+ and U-Net++ with EfficientNet-B3, diagnostic evaluation and threshold tuning.