Image Preprocessing

How to Convert Annotations from PASCAL VOC to YOLO Darknet

A bedrock of computer vision is having labeled data. In object detection problems, those labels define bounding box positions in a given image. As computer vision rapidly evolves, so, too,

When to Use Contrast as a Preprocessing Step

Adding contrast to images is a simple yet powerful technique to improve our computer vision models. But why? When considering how to add contrast to images and why we add

When Should I Auto-Orient My Images?

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

Breaking Down Roboflow's Health Check Dimension Insights

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 Difference Between Missing and Null Annotations

A discussion of missing versus null annotations and how VOC XML and COCO JSON handle them. Preparing data for computer vision models is a tedious task. Even assuming training images

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

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?

You Might Be Resizing Your Images Incorrectly

Resizing images is a critical preprocessing step in computer vision. Principally, our machine learning models train faster on smaller images. An input image that is twice as large requires our

How to Convert Annotations from PASCAL VOC XML to COCO JSON

Convert from VOC XML to COCO JSON (or any format!) in four clicks.

Why Image Preprocessing and Augmentation Matters

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