Synthetic Datasets with Blender

Deep Learning is an incredible tool, but only if you can train it. Due to the unprecedented need for massive, annotated, image datasets, many AI engineers have hit a serious roadblock: Training data is either extremely time consuming to create or expensive to pay others for their time. Fortunately, there is another way! A workaround that’s actually scalable (and way more fun). First, check out this overview video series. Then, scroll down for a free course to get you well on your way.

• Part 1: What synthetic image datasets are, why Blender is a great tool to make them, and why these skills are extremely valuable to start building now.

• Part 2: I break down the building blocks of creating synthetic datasets: rendering realistic 3D generated images. I also explain, again, why Blender is a great tool for this process.

• Part 3: I will be addressing accessibility, annotation, automation, and scalability. Annotation is how we go from a bunch of pretty pictures to an actual dataset that can be used to train AI. Automation and scalability are how we make millions of images and accessibility is what opens this process up for everyone to use.

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Want to Learn Blender for Creating Synthetic Data?

We’ve created a course that will teach you the fundamentals of Blender and walk you through the creation of a synthetic dataset, then training AI and using it on real photos. The videos are now completely free on YouTube and we’ve made the downloadable assets super affordable. Check out the video and link below. ⬇

Here’s a direct link to the course page: 3D Rendered Datasets in Blender for Beginners

Thanks for watching!