Our First Video Tutorial: YOLOv3 in PyTorch on a Custom Dataset

We're introducing a new experiment this week:

Roboflow is launching a YouTube channel.

We've been encouraged by the popularity of our computer vision tutorials. When Googling for some architectures, Roboflow content ranks at the top of what researchers, developers, and aspiring computer vision practitioners are finding valuable.

Increasingly, we've had our users ask us for a more guided approach to our tutorials. Initially, we were able to guide users with individual feedback. But the volume has grown, as have the nature of the requests. It's time to experiment with a new approach.

Roboflow will be narrating tutorials we publish to our model library, new features, and more on YouTube. We're hoping this serves as a more engaging way to provide the support our users have come to expect from us. In time, it may even become a valued growth channel.

Our debut video demonstrates how to use our PyTorch YOLOv3 implementation, leveraging an implementation from Ultralytics, but making it even easier to use your own dataset for training. We continue to encourage feedback through Roboflow Support and help@roboflow.ai.

While we don't anticipate ever reaching the trending page, we humbly ask that you might "smash that like and subscribe button" in the interim.

Want to be the first to know about new computer vision tutorials and content like our synthetic dataset creation guide? Subscribe to our updates 📬.

Roboflow accelerates your computer vision workflow through automated annotation quality assurance, universal annotation format conversion (like PASCAL VOC XML to COCO JSON and creating TFRecords ), team sharing and versioning, and easy integration with popular open source computer vision models. Getting started with your first 1GB of data is completely free.

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