53 lines
2.9 KiB
Markdown
53 lines
2.9 KiB
Markdown
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---
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comments: true
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description: Learn how to use Roboflow for organizing, labelling, preparing, and hosting your datasets for YOLOv5 models. Enhance your model deployments with our platform.
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keywords: Ultralytics, YOLOv5, Roboflow, data organization, data labelling, data preparation, model deployment, active learning, machine learning pipeline
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---
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# Roboflow Datasets
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You can now use Roboflow to organize, label, prepare, version, and host your datasets for training YOLOv5 🚀 models. Roboflow is free to use with YOLOv5 if you make your workspace public. UPDATED 7 June 2023.
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!!! Warning
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Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.
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## Upload
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You can upload your data to Roboflow via [web UI](https://docs.roboflow.com/adding-data), [REST API](https://docs.roboflow.com/adding-data/upload-api), or [Python](https://docs.roboflow.com/python).
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## Labeling
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After uploading data to Roboflow, you can label your data and review previous labels.
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[](https://roboflow.com/annotate)
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## Versioning
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You can make versions of your dataset with different preprocessing and offline augmentation options. YOLOv5 does online augmentations natively, so be intentional when layering Roboflow's offline augs on top.
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## Exporting Data
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You can download your data in YOLOv5 format to quickly begin training.
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```
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from roboflow import Roboflow
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rf = Roboflow(api_key="YOUR API KEY HERE")
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project = rf.workspace().project("YOUR PROJECT")
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dataset = project.version("YOUR VERSION").download("yolov5")
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```
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## Custom Training
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We have released a custom training tutorial demonstrating all of the above capabilities. You can access the code here:
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[](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)
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## Active Learning
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The real world is messy and your model will invariably encounter situations your dataset didn't anticipate. Using [active learning](https://blog.roboflow.com/what-is-active-learning/) is an important strategy to iteratively improve your dataset and model. With the Roboflow and YOLOv5 integration, you can quickly make improvements on your model deployments by using a battle tested machine learning pipeline.
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<p align=""><a href="https://roboflow.com/?ref=ultralytics"><img width="1000" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="Roboflow active learning"></a></p>
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