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 between those collections of pixels denote an object is in-frame. But machine learning models (like convolutional neural networks) have a…

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 can improve your deep learning model’s performance -- without needing to collect anymore data or even re-label images. We’…

Getting Started with LabelImg for Labeling Object Detection Data

Accurately labeled data is essential to successful machine learning, and computer vision is no exception. In this walkthrough, we’ll demonstrate how you can use LabelImg to get started with labeling your own data for object detection problems. About LabelImgLabelImg is a free, open source tool for graphically labeling images.…

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 deep learning models in production aren't so lucky. Deliberately introducing imperfections into our datasets is essential to making our machine…

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: build deep learning models that predict a world that you may have not anticipated. But that's what is at the…

How to Select the Right Computer Vision Model Architecture

The success of your machine learning model starts well before you ever begin training. Ensuring you have representative images, high quality labels, appropriate preprocessing steps, and augmentations to guard against overfitting all affect deep learning model performance well before you begin training. And this is where Roboflow Organize comes in…