Applying data augmentations is one of the most essential steps when developing your dataset. Roboflow offers a wide variety of augmentations that you can apply to your dataset including an
One of the amazing things about computer vision is using existing images plus random changes to increase your effective sample size. Suppose you have one photo containing a coffee mug.
When creating computer vision models, data augmentation can improve model performance with an existing image dataset. Image augmentation increases the size and variability of a dataset, thereby improving model generalizability.
Collecting images and annotating them with high-quality labels can be an expensive and time-consuming process. The promise of generating synthetic data to reduce the burden is alluring. In the past
Ok, so you've trained a model and it's not doing as well as you'd hoped. Now what? You could experiment with augmentation, try a different architecture, or check your training
Suppose you're trying to teach an alien – like one of the crewmates from the wildly popular game Among Us – to tell the difference between a human and a dog. "Purp
Machine learning algorithms are exceptionally data-hungry, requiring thousands – if not millions – of examples to make informed decisions. Providing high quality training data for our algorithms to learn is an expensive
Today, we introduce a new and improved shear augmentation. We'll walk through some details on the change, as well as some intuition and results backing up our reasoning.
Roboflow is constantly improving how developers can build better computer vision models based on better input data. One key piece to this puzzle is enabling users to select augmentations that
Detecting small objects is one of the most challenging and important problems in computer vision. In this post, we will discuss some of the strategies we have developed at Roboflow
Computer vision data augmentation is a powerful way to improve the performance of our computer vision models without needing to collect additional data. We create new versions of our images
The "Secret" to YOLOv4 isn't Architecture: It's in Data PreparationThe object detection space continues to move quickly. No more than two months ago, the Google Brain team released EfficientDet for
The appeals of synthetic data are alluring: you can rapidly generate a vast amount of diverse, perfectly labeled images for very little cost and without ever leaving the comfort of your office. The good news is: it's easy to try! And we're about to show you how.
Knowing how an image preprocessing step or augmentation is going to appear before you write the code for it is essential. Is it worth it to figure out the right
Flipping an image (and its annotations) is a deceivingly simple technique that can improve model performance in substantial ways. Our models are learning what collection of pixels and the relationship
Having training data that matches the diversity of your task is paramount to the success of your models. At Roboflow, we’re committed to providing you with state-of-the-art techniques that
When we train computer vision models, we often take ideal photos of our subjects. We line up our subject just right and curate datasets of best case lighting. But our
We seek to build computer vision models that generalize to as many real world situations as we can, even when we cannot anticipate them. It's a bit of a catch-22:
