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YOLOv5 with 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. UPDATED 30 September 2021.

Upload

You can upload your data to Roboflow via web UI, rest API, or python.

Labeling

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

Roboflow Annotate

Versioning

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.

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.