Image Augmentation

The Train, Validation, Test Split and Why You Need It

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

Tackling the Small Object Problem in Object Detection

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

Why and How to Implement Random Rotate Data Augmentation

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

Data Augmentation in YOLOv4

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

How to Create a Synthetic Dataset for Computer Vision

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.

Introducing Image Preprocessing and Augmentation Previews

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

How Flip Augmentation Improves Model Performance

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

Introducing Bounding Box Level Augmentations

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

The Importance of Blur as an Image Augmentation Technique

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

Why to Add Noise to Images for Machine Learning

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:

Why and How to Implement Random Crop Data Augmentation

We can’t capture a photo of what every object looks like in the real world. (Trying to find an image to prove the prior sentence is a fun paradox!

When to Use Grayscale as a Preprocessing Step

Grayscale allows our models to be more computationally efficient. So when **shouldn't** we grayscale our images?

Why Image Preprocessing and Augmentation Matters

Understanding image preprocessing and augmentation options is essential to making the most of your training data.