Image Augmentation

What is a Cutout Augmentation and When Can it Help?

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

The power of image augmentation: an experiment

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.

Image Augmentations for Aerial Datasets

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.

Using Unity Perception to train an object detection model with synthetically generated images

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

Using Public Datasets to Improve your Computer Vision Models

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

5 Strategies for Handling Unbalanced Classes

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

What is Active Learning?

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

Introducing an Improved Shear Augmentation

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.

Introducing Grayscale and Hue/Saturation Augmentations

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

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 Matter

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