--- comments: true description: Discover the YOLOv5 object detection model designed to deliver fast and accurate real-time results. Let's dive into this documentation to harness its full potential! --- # Ultralytics YOLOv5

YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

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](tutorials/train_custom_data.md) 🚀 RECOMMENDED * [Tips for Best Training Results](tutorials/tips_for_best_training_results.md) ☘️ * [Multi-GPU Training](tutorials/multi_gpu_training.md) * [PyTorch Hub](tutorials/pytorch_hub_model_loading.md) 🌟 NEW * [TFLite, ONNX, CoreML, TensorRT Export](tutorials/model_export.md) 🚀 * [NVIDIA Jetson platform Deployment](tutorials/running_on_jetson_nano.md) 🌟 NEW * [Test-Time Augmentation (TTA)](tutorials/test_time_augmentation.md) * [Model Ensembling](tutorials/model_ensembling.md) * [Model Pruning/Sparsity](tutorials/model_pruning_and_sparsity.md) * [Hyperparameter Evolution](tutorials/hyperparameter_evolution.md) * [Transfer Learning with Frozen Layers](tutorials/transfer_learning_with_frozen_layers.md) * [Architecture Summary](tutorials/architecture_description.md) 🌟 NEW * [Roboflow for Datasets, Labeling, and Active Learning](tutorials/roboflow_datasets_integration.md) * [ClearML Logging](tutorials/clearml_logging_integration.md) 🌟 NEW * [YOLOv5 with Neural Magic's Deepsparse](tutorials/neural_magic_pruning_quantization.md) 🌟 NEW * [Comet Logging](tutorials/comet_logging_integration.md) 🌟 NEW ## Environments YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md) - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md) - **Docker Image**. See [Docker Quickstart Guide](environments/docker_image_quickstart_tutorial.md) Docker Pulls ## Status YOLOv5 CI If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.