The Best segmentation models.pytorch Alternatives in 2026

This repository provides segmentation models with pretrained backbones for Keras and TensorFlow Keras.

Ultralytics YOLOv5 supports object detection, instance segmentation, classification, training, and export in PyTorch.

This repository implements YOLOv3 variants in PyTorch for real time object detection, training, validation, inference, and export.

The lightly Python library supports self supervised learning on images.

Ultralytics supports detection, segmentation, image classification, pose estimation, and tracking across YOLO model versions.

Detectron2 provides computer vision tools for object detection, segmentation, and other visual recognition tasks.

This TabGAN repository reviews research on applying GANs to generate tabular data.

pytorch tabular provides a unified framework for deep learning models on tabular data.

Captum explains model behavior and supports interpretability analysis for PyTorch systems.

PyTorch Frame provides a library for applying deep learning models to tabular data.

A Python package to assess and improve fairness of machine learning models.

WhichLLM tests local language models on a user’s hardware and ranks them using recent benchmarks.

FastDatasets helps create training datasets for large language models.

AIF360 provides fairness metrics and explanations for datasets and models, along with algorithms to mitigate bias.

Automatic "Differentiation" via Text, using large language models to backpropagate textual gradients.

Giskard provides open source evaluation and testing tools for LLM agents.

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

Cornac provides a framework for comparing multimodal recommender systems and recommendation algorithms.

Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.

nilearn provides Python tools for machine learning and neuroimaging analysis.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.