We heard your feedback!
More video walkthroughs. Many users report that video tutorials help round out the edges of their knowledge to get the most from Roboflow. Seeing how others use Roboflow in real-time aids their own comprehension.
Make YOLOv4 more accessible. YOLOv4 is a mere month old, and given the records it has shattered, we're not surprised to see booming interest in the architecture. We've written about data augmentation in YOLOv4, how to train YOLOv4 using the Darknet framework, and a deep dive into the YOLOv4 architecture.
YOLOv5 is Out!
Consider jumping right to our post on How To Train YOLOv5. You'll have a trained YOLOv5 model on your custom data in a matter of minutes.
Now, we're introducing a comprehensive walkthrough on using Roboflow to train your own YOLOv4 model using an even more popular framework: PyTorch. We're first sharing the walkthrough in the form of a comprehensive 20-minute YOLOv4 video tutorial, and we'll soon have a drafted written article to follow along, too.
Keep the feedback coming! We're listening.
Roboflow accelerates your computer vision workflow through automated annotation
quality assurance, universal annotation format conversion (like
PASCAL VOC XML to COCO JSON and
), team sharing and versioning, and easy integration with popular
open source computer vision models.
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