README and Docs updates (#166)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>single_channel
Ayush Chaurasia
2 years ago
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GitHub
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[](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml)
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<div align="center">
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<p>
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<a align="center" href="https://ultralytics.com/yolov8" target="_blank">
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<img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
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</p>
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## Install
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[English](README.md) | [简体中文](README.zh-CN.md)
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<br>
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```bash
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pip install ultralytics
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```
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<div>
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
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<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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<br>
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<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
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<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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</div>
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<br>
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Development
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[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO object detection and image segmentation model developed by [Ultralytics](https://ultralytics.com). YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility.
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```
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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pip install -e .
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```
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The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
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Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page will help you get the most out of YOLOv8.
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## Usage
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<div align="center">
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
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</div>
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</div>
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### 1. CLI
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## <div align="center">Documentation</div>
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To simply use the latest Ultralytics YOLO models
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See below for quickstart intallation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
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<details open>
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<summary>Install</summary>
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Pip install the ultralytics package including all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
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[**Python>=3.7.0**](https://www.python.org/) environment, including
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[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
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```bash
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yolo task=detect mode=train model=yolov8n.yaml args=...
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classify predict yolov8n-cls.yaml args=...
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segment val yolov8n-seg.yaml args=...
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export yolov8n.pt format=onnx
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pip install ultralytics
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```
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### 2. Python SDK
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</details>
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To use pythonic interface of Ultralytics YOLO model
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<details open>
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<summary>Usage</summary>
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YOLOv8 may be used in a python environment:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml") # create a new model from scratch
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model = YOLO(
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"yolov8n.pt"
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) # load a pretrained model (recommended for best training results)
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results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
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results = model.val()
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results = model.predict(source="bus.jpg")
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success = model.export(format="onnx")
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model = YOLO("yolov8n.pt") # load a pretrained YOLOv8n model
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model.train(data="coco128.yaml") # train the model
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model.val() # evaluate model performance on the validation set
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model.predict(source="https://ultralytics.com/images/bus.jpg") # predict on an image
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model.export(format="onnx") # export the model to ONNX format
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```
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Or with CLI `yolo` commands:
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```bash
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yolo task=detect mode=train model=yolov8n.pt args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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```
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## Models
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 28.0 | - | - | **1.9** | **4.5** |
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| [YOLOv6n](url) | 640 | 35.9 | - | - | 4.3 | 11.1 |
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| **[YOLOv8n](url)** | 640 | **37.3** | - | - | 3.2 | 8.9 |
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| | | | | | | |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 37.4 | - | - | 7.2 | 16.5 |
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| [YOLOv6s](url) | 640 | 43.5 | - | - | 17.2 | 44.2 |
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| **[YOLOv8s](url)** | 640 | **44.9** | - | - | 11.2 | 28.8 |
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| | | | | | | |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 45.4 | - | - | 21.2 | 49.0 |
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| [YOLOv6m](url) | 640 | 49.5 | - | - | 34.3 | 82.2 |
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| **[YOLOv8m](url)** | 640 | **50.2** | - | - | 25.9 | 79.3 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 49.0 | - | - | 46.5 | 109.1 |
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| [YOLOv6l](url) | 640 | 52.5 | - | - | 58.5 | 144.0 |
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| [YOLOv7](url) | 640 | 51.2 | - | - | 36.9 | 104.7 |
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| **[YOLOv8l](url)** | 640 | **52.9** | - | - | 43.7 | 165.7 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 50.7 | - | - | 86.7 | 205.7 |
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| [YOLOv7-X](url) | 640 | 52.9 | - | - | 71.3 | 189.9 |
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| **[YOLOv8x](url)** | 640 | **53.9** | - | - | 68.2 | 258.5 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt) | 1280 | 55.0 | - | - | 140.7 | 839.2 |
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| [YOLOv7-E6E](url) | 1280 | 56.8 | - | - | 151.7 | 843.2 |
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| **[YOLOv8x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)**<br>+TTA | 1280 | -<br>- | -<br>- | -<br>- | 97.4 | 1047.2<br>- |
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If you're looking to modify YOLO for R&D or to build on top of it, refer to [Using Trainer](<>) Guide on our docs.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/ultralytics/releases).
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</details>
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## <div align="center">Checkpoints</div>
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All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset, while Classification models are pretrained on the ImageNet dataset.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on first use.
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<details open><summary>Detection</summary>
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
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<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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<details><summary>Segmentation</summary>
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| --------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
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<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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<details><summary>Classification</summary>
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| --------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
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<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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## <div align="center">Integrations</div>
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<br>
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<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a>
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<br>
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<br>
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<div align="center">
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<a href="https://roboflow.com/?ref=ultralytics">
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||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://cutt.ly/yolov5-readme-clearml">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-readme-comet">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-comet.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-neuralmagic">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-neuralmagic.png" width="10%" /></a>
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</div>
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv8 models, resume training, and interactively visualise and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
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## <div align="center">Ultralytics HUB</div>
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv8 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on your iOS or Android device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
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||||
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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## <div align="center">Contribute</div>
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We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
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<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
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|
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<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png"/></a>
|
||||
|
||||
## <div align="center">License</div>
|
||||
|
||||
YOLOv8 is available under two different licenses:
|
||||
|
||||
- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
|
||||
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
|
||||
|
||||
## <div align="center">Contact</div>
|
||||
|
||||
For YOLOv8 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For professional support please [Contact Us](https://ultralytics.com/contact).
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
|
||||
</div>
|
||||
|
||||
@ -0,0 +1,80 @@
|
||||
# Ultralytics HUB
|
||||
|
||||
<div align="center">
|
||||
<a href="https://hub.ultralytics.com" target="_blank">
|
||||
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
|
||||
<br>
|
||||
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
|
||||
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com) is a new no-code online tool developed
|
||||
by [Ultralytics](https://ultralytics.com), the creators of the popular [YOLOv5](https://github.com/ultralytics/yolov5)
|
||||
object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLOv5 models
|
||||
without any coding or technical expertise.
|
||||
|
||||
Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to
|
||||
easily upload their data and select their model configurations. It also offers a range of pre-trained models and
|
||||
templates to choose from, making it easy for users to get started with training their own models. Once a model is
|
||||
trained, it can be easily deployed and used for real-time object detection and image segmentation tasks. Overall,
|
||||
Ultralytics HUB is an essential tool for anyone looking to use YOLOv5 for their object detection and image segmentation
|
||||
projects.
|
||||
|
||||
**[Get started now](https://hub.ultralytics.com)** and experience the power and simplicity of Ultralytics HUB for yourself. Sign up for a free account and
|
||||
start building, training, and deploying YOLOv5 and YOLOv8 models today.
|
||||
|
||||
|
||||
## 1. Upload a Dataset
|
||||
|
||||
Ultralytics HUB datasets are just like YOLOv5 🚀 datasets, they use the same structure and the same label formats to keep everything simple.
|
||||
|
||||
When you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML inside the dataset root directory** as in the example shown below, and then zip for upload to https://hub.ultralytics.com/. Your **dataset YAML, directory and zip** should all share the same name. For example, if your dataset is called 'coco6' as in our example [ultralytics/hub/coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip), then you should have a coco6.yaml inside your coco6/ directory, which should zip to create coco6.zip for upload:
|
||||
|
||||
```bash
|
||||
zip -r coco6.zip coco6
|
||||
```
|
||||
|
||||
The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be downloaded and unzipped to see exactly how to structure your custom dataset.
|
||||
|
||||
<p align="center"><img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" /></p>
|
||||
|
||||
The dataset YAML is the same standard YOLOv5 YAML format. See the [YOLOv5 Train Custom Data tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) for full details.
|
||||
```yaml
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: # dataset root dir (leave empty for HUB)
|
||||
train: images/train # train images (relative to 'path') 8 images
|
||||
val: images/val # val images (relative to 'path') 8 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
...
|
||||
```
|
||||
|
||||
After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab. Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it!
|
||||
|
||||
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/198611715-540c9856-49d7-4069-a2fd-7c9eb70e772e.png">
|
||||
|
||||
|
||||
## 2. Train a Model
|
||||
|
||||
Connect to the Ultralytics HUB notebook and use your model API key to begin training! <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
|
||||
|
||||
## 3. Deploy to Real World
|
||||
|
||||
Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
|
||||
|
||||
<a align="center" href="https://ultralytics.com/app_install" target="_blank">
|
||||
<img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
|
||||
|
||||
|
||||
## ❓ Issues
|
||||
|
||||
If you are a new [Ultralytics HUB](https://bit.ly/ultralytics_hub) user and have questions or comments, you are in the right place! Please raise a [New Issue](https://github.com/ultralytics/hub/issues/new/choose) and let us know what we can do to make your life better 😃!
|
||||
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