Ultralytics is a production-grade computer vision library built around the YOLO model family. In production it provides a unified Python/CLI API for loading pretrained and custom models, running inference, training, validation, export, benchmarking, and object tracking across detection, instance and semantic segmentation, pose estimation, oriented bounding boxes, classification, and depth estimation. The architecture is a modular Python package: an engine layer (Model, BasePredictor, BaseTrainer, BaseValidator), a unified AutoBackend inference dispatcher supporting 20+ formats from PyTorch to TensorRT/ONNX/OpenVINO/CoreML/TFLite, a YAML-driven model configuration system, dataset loaders with caching and augmentation, pluggable experiment-tracking callbacks, and a family of ready-to-use solution classes for counting, heatmaps, queue management, and similar vision applications. The CLI entrypoint (`yolo`) and Python classes (`YOLO`, `SAM`, `RTDETR`, etc.) are the primary entry points, backed by active CI, Docker images, and extensive examples in Python, C++, and Rust.
MetaSeg is a Python library that packages Facebook's Segment Anything Model (SAM) into an easy-to-use interface for automatic and manual image/video segmentation. It provides high-level predictors (SegAutoMaskPredictor, SegManualMaskPredictor) that handle model loading, inference, and output visualization. The library integrates with SAHI for sliced inference and FalAI for cloud GPU execution, and includes a Gradio web application. The codebase is primarily a thin orchestration layer over the official SAM implementation, with additional utility functions for data loading, video processing, and model download. It is designed for developers who want to quickly apply SAM without deep model internals.