Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

A ComfyUI custom node designed for advanced image background removal and object, face, clothes, and fashion segmentation, utilizing multiple models including RMBG-2.0, INSPYRENET, BEN, BEN2, BiRefNet, SDMatte, SAM, SAM2, SAM3 and GroundingDINO.

:art: Semantic segmentation models, datasets and losses implemented in PyTorch.

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

SOTA Semantic Segmentation Models in PyTorch

Official PyTorch implementation of ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [CVPR 2023 Highlight]

Cross-Platform Production-ready C++ inference engine for YOLO models (v5-v12, YOLO26). Unified API for detection, segmentation, pose estimation, OBB, and classification. Built on ONNX Runtime and OpenCV. Optimized for CPU/GPU with quantization support.

Label-Efficient Semantic Segmentation with Diffusion Models (ICLR'2022)

Implementation of web friendly ML models using TensorFlow.js. pix2pix, face segmentation, fast style transfer and many more ...

A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.

Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning

Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

A list of popular deep learning models related to classification, segmentation and detection problems

EfficientSAM3 compresses SAM3 into lightweight, edge-friendly models via progressive knowledge distillation for fast promptable concept segmentation and tracking.

[ECCV 2022] Official repository for "MaxViT: Multi-Axis Vision Transformer". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling...

I will upload many semantic segmentation models to this repository for you to learn

[CVPR 2024] Official implement of <Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation>

Facilitates simple large scale processing of HLS Medical images, documents, zip files. OHIF Viewer, 2 segmentation models and interactive learning.

Lightweight models for real-time semantic segmentation(include mobilenetv1-v3, shufflenetv1-v2, igcv3, efficientnet).

[ECCV 2020] PyTorch Implementation of some RGBD Semantic Segmentation models.

This is the official Pytorch implementation of the paper "Diffusion Models for Implicit Image Segmentation Ensembles".

Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).

Official repository for Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation. (DINOv3)

PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation, distributed training, Optuna etc.