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.
Roboflow users can use Ultralytics under the AGPL license or procure an Enterprise 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.
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 augmentations on top.
You can download your data in YOLOv5 format to quickly begin training.
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.
- Free GPU Notebooks:
- Google Cloud: GCP Quickstart Guide
- Amazon: AWS Quickstart Guide
- Azure: AzureML Quickstart Guide
- Docker: Docker Quickstart Guide
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 2023-12-03
Authors: glenn-jocher (4)