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.
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.
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.
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")
We have released a custom training tutorial demonstrating all of the above capabilities. You can access the code here:
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.
Created 2023-04-21, Updated 2023-05-09
Authors: Glenn Jocher (3)