The Best Yolov5 Alternatives in 2026

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

This repository provides semantic segmentation models with more than 500 pretrained convolutional and transformer backbones.

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

The lightly Python library supports self supervised learning on images.

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

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

Snorkel helps generate training data programmatically through weak supervision.

PySS3 supports interpretable text classification with SS3 and visualization tools for explainable AI.

FastDatasets helps create training datasets for large language models.

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.

PyTorch Geometric provides graph neural network tools and libraries for PyTorch projects.

GPyTorch provides an efficient implementation of Gaussian processes using PyTorch.

An open source Python library for drift detection in machine learning systems.

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.

Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.

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.