Metadata-Version: 2.1
Name: ultralytics
Version: 0.0.36
Home-page: https://github.com/ultralytics/yolov5
Author: Ultralytics
Author-email: hello@ultralytics.com
Keywords: machine-learning,deep-learning,ML,AI,PyTorch,object-detection,vision,YOLO,YOLOv3,YOLOv4,YOLOv5
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: PyYAML (>=5.3.1)
Requires-Dist: requests
Requires-Dist: GitPython (>=3.1.24)
Requires-Dist: torch (>=1.7.0)
Requires-Dist: torchvision (>=0.8.1)
Requires-Dist: psutil
Requires-Dist: IPython
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'
Requires-Dist: coverage ; extra == 'tests'

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👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://ultralytics.com/hub), a new web tool for training and deploying all your YOLOv5 🚀 models from one spot!


## 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/26833433/198611465-912f8673-c83f-471e-85a4-9d070f750f16.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://hub.ultralytics.com) 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://user-images.githubusercontent.com/26833433/198139156-924f56da-4d18-49d0-932d-5a815022f4fa.png">


## ❓ Issues

If you are a new [Ultralytics HUB](https://ultralytics.com/hub) user and have questions or comments, you are in the right place! Please click the [New Issue](https://github.com/ultralytics/hub/issues/new/choose) button in the [Issues](https://github.com/ultralytics/hub/issues) tab in this repo and let us know what we can do to make your life better 😃!


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