Image Augmentation

What is Data Augmentation? The Ultimate Guide.

In this guide, we talk about what data augmentation is, how augmented data can boost model performance, and how augmentations are used in computer vision.

How to Reduce Dataset Size Without Losing Accuracy

We're often told that data is the backbone that drives the development of powerful and robust models.  And that's certainly true – data is the raw material that we feed into

Using Stable Diffusion and SAM to Modify Image Contents Zero Shot

Introduction Recent breakthroughs in large language models (LLMs) and foundation computer vision models have unlocked new interfaces and methods for editing images or videos. You may have heard of inpainting,

How to Use Generative AI to Augment Computer Vision Data

Dive deep into, a tool for generative data augmentation created by to improve the quality of datasets.

Not All mAPs are Equal and How to Test Model Robustness

Learn how to stress-test the robustness of computer vision models.

School Bus Detection Using YOLOv5 (Tutorial – Part 2)

This is a guest post by Kristen Kehrer [] , Developer Advocate at CometML [].  Since

Building Custom Computer Vision Models with NVIDIA TAO Toolkit and Roboflow

NVIDIA's TAO Toolkit provides a framework for fine-tuning popular computer vision models using your own data. In this tutorial, we'll be demonstrating how to use Roboflow to curate a high-quality computer vision dataset to use with NVIDIA's TAO Toolkit.

Using Polygon Annotations for Object Detection in Computer Vision

Benefits to Existing Models Polygons have traditionally been used for training image segmentation models [], but polygons can also improve the training of object detection models

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

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

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 [https://blog.roboflow.

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

What is Active Learning? The Ultimate Guide.

In this guide, we discuss what active learning is, types of active learning, and walk through an example of active learning in practice.

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

How to Detect Small Objects: A Guide

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

Data Augmentation in YOLOv4

The "secret" to YOLOv4 isn't architecture: it's in data preparation. The object detection space [] continues to move quickly. No more than two months ago, the

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

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

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 [https://public.