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YOLOv5

Object Detection
ultralytics
yolo
yolov5
instance-segmentation
image-classification
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+ ---
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+ license: agpl-3.0
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+ language:
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+ - en
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+ - zh
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+ - ja
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+ - ru
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+ - de
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+ - fr
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+ - es
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+ - pt
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+ - tr
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+ - vi
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+ - ar
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+ library_name: ultralytics
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+ pipeline_tag: object-detection
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+ tags:
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+ - ultralytics
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+ - yolo
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+ - yolov5
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+ - object-detection
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+ - instance-segmentation
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+ - image-classification
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+ ---
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+
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+ [![Ultralytics YOLO banner](https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png)](https://platform.ultralytics.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=platform_launch&utm_content=banner&utm_term=ultralytics_github)
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+
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+ [中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
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+
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+ [![Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml) [![Ultralytics Downloads](https://img.shields.io/pepy/dt/ultralytics?color=blue)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://www.reddit.com/r/ultralytics/)
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+
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+ [![Run on Gradient](https://img.shields.io/badge/Run_on-Gradient-blue?logo=paperspace&logoColor=white)](https://console.paperspace.com/github/ultralytics/ultralytics) [![Open In Colab](https://img.shields.io/badge/Open_in-Colab-blue?logo=googlecolab&logoColor=white)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) [![Open In Kaggle](https://img.shields.io/badge/Open_in-Kaggle-blue?logo=kaggle&logoColor=white)](https://www.kaggle.com/models/ultralytics/yolov5) [![Launch Binder](https://img.shields.io/badge/Launch-Binder-blue?logo=jupyter&logoColor=white)](https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb)
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+
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+ [Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https://docs.ultralytics.com/tasks/detect), [tracking](https://docs.ultralytics.com/modes/track), [instance segmentation](https://docs.ultralytics.com/tasks/segment), [semantic segmentation](https://docs.ultralytics.com/tasks/semantic), [image classification](https://docs.ultralytics.com/tasks/classify), and [pose estimation](https://docs.ultralytics.com/tasks/pose) tasks.
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+
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+ Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!
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+
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+ Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).
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+
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+ ## 📄 Documentation
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+
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+ See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https://docs.ultralytics.com/).
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+
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+ <details open>
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+ <summary>Install</summary>
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+
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+ Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
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+
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+ [![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://img.shields.io/pepy/dt/ultralytics?color=blue)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)
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+
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+ ```bash
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+ pip install ultralytics
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+ ```
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+
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+ For alternative installation methods, including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
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+
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+ [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
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+
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+ </details>
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+
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+ <details open>
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+ <summary>Usage</summary>
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+
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+ ### CLI
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+
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+ You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
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+
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+ ```bash
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+ # Predict using a pretrained YOLO model (e.g., YOLOv5n) on an image
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+ yolo predict model=yolov5n.pt source='https://ultralytics.com/images/bus.jpg'
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+ ```
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+
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+ The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli) for more examples.
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+
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+ ### Python
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+
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+ Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg) as the CLI:
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+
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+ ```python
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+ from ultralytics import YOLO
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+
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+ # Load a pretrained YOLOv5n model
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+ model = YOLO("yolov5n.pt")
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+
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+ # Train the model on the COCO8 dataset for 100 epochs
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+ train_results = model.train(
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+ data="coco8.yaml", # Path to dataset configuration file
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+ epochs=100, # Number of training epochs
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+ imgsz=640, # Image size for training
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+ device="cpu", # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
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+ )
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+
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+ # Evaluate the model's performance on the validation set
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+ metrics = model.val()
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+
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+ # Perform object detection on an image
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+ results = model("path/to/image.jpg") # Predict on an image
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+ results[0].show() # Display results
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+
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+ # Export the model to ONNX format for deployment
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+ path = model.export(format="onnx") # Returns the path to the exported model
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+ ```
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+
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+ Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python).
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+
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+ </details>
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+
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+ ## ✨ Models
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+
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+ Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3) to the latest [YOLO26](https://docs.ultralytics.com/models/yolo26). The tables below showcase YOLOv5 models pretrained on [COCO](https://docs.ultralytics.com/datasets/detect/coco) for [Detection](https://docs.ultralytics.com/tasks/detect) and [Segmentation](https://docs.ultralytics.com/tasks/segment), and [Classification](https://docs.ultralytics.com/tasks/classify) models pretrained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet). [Tracking](https://docs.ultralytics.com/modes/track) mode is compatible with Detection and Segmentation models. All [Models](https://docs.ultralytics.com/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
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+
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+ <a href="https://docs.ultralytics.com/tasks" target="_blank">
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+ <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
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+ </a>
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+ <br>
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+ <br>
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+
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+ <details open><summary>Detection (COCO)</summary>
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+
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+ Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes.
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+
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+ | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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+ | ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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+ | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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+ | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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+ | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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+ | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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+ | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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+ | | | | | | | | | |
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+ | [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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+ | [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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+ | [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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+ | [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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+ | [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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+
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+ - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
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+ - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
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+ - **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
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+ - **TTA** [Test Time Augmentation][tta] includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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+
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+ </details>
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+
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+ <details><summary>Segmentation (COCO)</summary>
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+
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+ Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco), including 80 classes.
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+
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+ | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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+ | ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
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+ | [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
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+ | [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
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+ | [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
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+ | [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
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+ | [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
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+
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+ - All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
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+ - **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
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+ - **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
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+ - **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
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+
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+ </details>
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+
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+ <details><summary>Classification (ImageNet)</summary>
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+
164
+ Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet), covering 1000 classes.
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+
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+ | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
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+ | -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
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+ | [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
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+ | [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
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+ | [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
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+ | [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
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+ | [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
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+ | | | | | | | | | |
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+ | [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
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+ | [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
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+ | [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
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+ | [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
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+ | | | | | | | | | |
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+ | [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
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+ | [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
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+ | [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
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+ | [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
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+
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+ - All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
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+ - **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
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+ - **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
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+ - **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
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+
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+ </details>
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+
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+ ## 🧩 Integrations
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+
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+ Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases), [Comet ML](https://docs.ultralytics.com/integrations/comet), [Roboflow](https://docs.ultralytics.com/integrations/roboflow), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations).
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+
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+ <a href="https://platform.ultralytics.com" target="_blank">
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+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
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+ </a>
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+
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+ ## 🤝 Contribute
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+
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+ We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=huggingface&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!
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+
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+ <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=1280 -->
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+
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+ [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
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+
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+ We look forward to your contributions to help make the Ultralytics ecosystem even better!
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+
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+ ## 📜 License
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+
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+ Ultralytics offers two licensing options to suit different needs:
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+
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+ - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details.
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+ - **Ultralytics Enterprise License**: For development and production use, this license enables seamless integration of Ultralytics software and AI models into business products and services, including internal tools, automated workflows, and production deployments, bypassing the open-source requirements of AGPL-3.0. To get started, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
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+
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+ ## 📞 Contact
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+
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+ For bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/). We're here to help with all things Ultralytics!
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+
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+ <br>
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+
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+ [tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation
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