Skip to content

Roboflow Datasets

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.


Ultralytics offers two licensing options:

For more details see Ultralytics Licensing.


You can upload your data to Roboflow via web UI, REST API, or Python.


After uploading data to Roboflow, you can label your data and review previous labels.

Roboflow Annotate


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 augmentations on top.

Roboflow Preprocessing

Exporting Data

You can download your data in YOLOv5 format to quickly begin training.

from roboflow import Roboflow
rf = Roboflow(api_key="YOUR API KEY HERE")
project = rf.workspace().project("YOUR PROJECT")
dataset = project.version("YOUR VERSION").download("yolov5")

Custom Training

We have released a custom training tutorial demonstrating all of the above capabilities. You can access the code here:

Open In Colab

Active Learning

The real world is messy and your model will invariably encounter situations your dataset didn't anticipate. Using 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.

Roboflow active learning

Supported Environments

Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as CUDA, CUDNN, Python, and PyTorch, to kickstart your projects.

Project Status


This badge indicates that all YOLOv5 GitHub Actions Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: training, validation, inference, export, and benchmarks. They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.

Created 2023-11-12, Updated 2024-01-21
Authors: glenn-jocher (6)