Dataset Management

8 Dec 2020 • 2 min read

Introducing Roboflow Annotate

Since we launched Roboflow in early 2020, our vision has always been to improve and streamline the workflow of computer vision projects so that developers can focus on the parts of their project that are unique, not on reinventing the wheel. But there's been one part of the

30 Nov 2020 • 5 min read

How to Run Jupyter Notebooks on an Apple M1 Mac

Learn how to run Jupyter Notebooks on Apple M1 Macbooks.

28 Oct 2020 • 2 min read

Train Test Split Guide and Overview

In order to ensure our models are generalizing well (rather than memorizing training data), it is best practice to create a train, test split. That is, absent rigor, our models can easily overfit to a small subset of examples we've collected. Look no further than Tesla using computer

12 Oct 2020 • 2 min read

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.

27 Sep 2020 • 3 min read

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 best improve dataset representation through augmentation. Augmentation creates altered training data based on existing examples. Image augmentation improves model performance,

25 Sep 2020 • 3 min read

Getting Started with VGG Image Annotator for Object Detection Tutorial

Annotating your images is easy using the free, open source VGG Image Annotator. In this post we will walk through the steps necessary to get up and running with the VGG Image Annotator so you can quickly, and efficiently label your computer vision dataset for object detection and move on

4 Sep 2020 • 6 min read

Train, Validation, Test Split for Machine Learning

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.

27 Jul 2020 • 6 min read

VoTT for Image Annotation and Labeling

A guide on using VoTT to label your own computer vision dataset.

24 Jun 2020 • 9 min read

Why and How to Implement Random Rotate Data Augmentation

Learn how to apply a random rotate data augmentation to images for use in training computer vision models.

19 Jun 2020 • 5 min read

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, do the various file formats available to describe the location of bounding boxes: PASCAL VOC XML, COCO JSON, various CSV

13 May 2020 • 7 min read

Data Augmentation in YOLOv4

Learn how data augmentation is used in training YOLOv4 computer vision models.

8 May 2020 • 2 min read

When Should I Auto-Orient My Images?

Learn when you should auto-orient images for use in training computer vision models.

29 Apr 2020 • 3 min read

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 users of potential areas requiring attention like severely underrepresented classes (as was present in the original hard hat object detection

24 Apr 2020 • 4 min read

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 are appropriately representative for inference, managing annotations quickly becomes a challenge. In some annotation formats (PASCAL VOC XML, YOLO DarkNet)

15 Apr 2020 • 12 min read

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.

6 Apr 2020 • 6 min read

How to Create to a TFRecord File for Computer Vision and Object Detection

TensorFlow expedites the machine learning process markedly. From abstracting complex linear algebra to including pre-trained models and weights, getting the most out of TensorFlow is a full-time job. However, when it comes to loading data in ways that TensorFlow expects in order to perform as efficiently as it does, every

30 Mar 2020 • 1 min read

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 amount of brightness? Will rotation increase variability appropriately? Roboflow is introducing features to take out the guesswork: preprocessing and augmentation

20 Mar 2020 • 2 min read

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

18 Mar 2020 • 3 min read

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 any more data or even re-label images.

16 Mar 2020 • 3 min read

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 models. Label and Annotate Data with Roboflow for free Use Roboflow to manage

13 Mar 2020 • 3 min read

The Importance of Blur as an Image Augmentation Technique

Learn about the efficacy of blur as an image augmentation step in computer vision model training.

9 Mar 2020 • 3 min read

Why to Add Noise to Images for Machine Learning

Learn why adding noise can be effective as an image augmentation in computer vision modeling.

21 Feb 2020 • 4 min read

Why and How to Implement Random Crop Data Augmentation

Learn how to apply a random crop data augmentation to images for use in training computer vision models.