Ultralytics YOLO

Ultralytics YOLO is an object detection model built on PyTorch for real-time computer vision applications.

Ultralytics YOLOv5 is an object detection model designed to deliver high-speed and high-accuracy results in real-time. Built on PyTorch, this deep learning framework offers versatility and ease of use for computer vision projects. The documentation guides users through installation, model architecture, custom data training, and deployment. It supports exports to TFLite, ONNX, CoreML, and TensorRT formats, along with integrations for ClearML and Comet logging. The framework is suited for developers and researchers building vision applications.

Ultralytics YOLO at a glance

Best for: Software engineer, Researcher
Runs on: macOS, Windows, Linux, self-hosted, open source
Pricing: Free
Released: May 2020
Category: AI image tool
Website: docs.ultralytics.com

Key features

Train custom data on custom datasets
Export models to TFLite, ONNX, CoreML, and TensorRT
Use Test-Time Augmentation for prediction accuracy
Combine multiple models via model ensembling
Automate hyperparameter tuning via evolution
Implement transfer learning with frozen layers

Use it for

Train custom object detection models
Export models for deployment
Optimize training with multiple GPUs
Log training metrics with ClearML and Comet

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