Computer vision is improving life sciences. From the early identification of cancer to improving plant health, machine vision is enabling us to create more accurate diagnoses, cures, and research methods.
At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model. T
The recommended Roboflow setting is "Auto-Orient: Enabled"When should you auto-orient your images?The short answer: almost always.When an image is captured, it contains metadata that dictates the orientation
Roboflow improves datasets without any user effort. This includes dropping zero-pixel bounding boxes and cropping out-of-frame bounding boxes to be in-line with the edge of an image. Roboflow also notifies
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.