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YOLOv5 Docs
Welcome to the Ultralytics YOLOv5 🚀 Docs! YOLOv5, or You Only Look Once version 5, is an Ultralytics object detection model designed to deliver fast and accurate real-time results.
This powerful deep learning framework is built on the PyTorch platform and has gained immense popularity due to its ease of use, high performance, and versatility. In this documentation, we will guide you through the installation process, explain the model's architecture, showcase various use-cases, and provide detailed tutorials to help you harness the full potential of YOLOv5 for your computer vision projects. Let's dive in!
Tutorials
- Train Custom Data 🚀 RECOMMENDED
- Tips for Best Training Results ☘️
- Multi-GPU Training
- PyTorch Hub 🌟 NEW
- TFLite, ONNX, CoreML, TensorRT Export 🚀
- NVIDIA Jetson platform Deployment 🌟 NEW
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layers
- Architecture Summary 🌟 NEW
- Roboflow for Datasets, Labeling, and Active Learning
- ClearML Logging 🌟 NEW
- YOLOv5 with Neural Magic's Deepsparse 🌟 NEW
- Comet Logging 🌟 NEW
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free
GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image.
See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
