Product Updates

3 Nov 2020 • 3 min read

Evaluating Object Detection Models with mAP by Class

When evaluating an object detection model in computer vision, mean average precision [https://blog.roboflow.com/mean-average-precision/] is the most commonly cited metric for assessing performance. Remember, mean average precision is a measure of our model's ability to correctly predict bounding boxes at some confidence level – commonly mAP@

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 [https://roboflow.com] 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

2 Sep 2020 • 2 min read

Explore Images, Annotations, and Metadata Easier

Keeping track of images and their corresponding annotations [https://blog.roboflow.com/glossary/#:~:text=annotation] is a challenge. Knowing which annotations map to which class, viewing image metadata, and seeing which images correspond to a training, validation, and testing set [https://blog.roboflow.com/train-test-split/] can quickly become a sprawling

21 Aug 2020 • 3 min read

The crazy story of how we got our .com domain

For almost a year, Roboflow (our computer vision dataset management tool) has lived at roboflow.ai. It's served us well but we have always lusted after the dot com. No longer! We managed to acquire roboflow.com from the giant German company that owned it since 2001. The

3 Aug 2020 • 5 min read

Ontology Management for Computer Vision

As their projects mature and dataset sizes grow, most teams wrestle with their workflow. Slicing and dicing data is more of an art than a science and you will want to experiment with what works best for your problem over time (and, in fact, you will probably go through this

29 Jul 2020 • 2 min read

Advanced Augmentations in Roboflow

Roboflow Pro now supports Cutout and Mosaic. Recent research has shown [https://arxiv.org/abs/2005.04757] there is still plenty of room to grow model performance through augmenting our training data. Roboflow has written extensively [https://blog.roboflow.com/tag/augmentation/] about data augmentation and has highlighted some of

23 Jul 2020 • 1 min read

Roboflow can now ingest video!

One of the most common questions we get is "How can I use computer vision object detection models with video?" The answer is simple: you treat each frame as an image. But, in practice, that has been quite cumbersome. It's meant fumbling around with command line

7 Jul 2020 • 3 min read

Introducing Class Label Remapping and Omission

With Roboflow Pro, you can now remap and omit class labels within Roboflow as a preprocessing step for your dataset version. Class management is a powerful tool to get the most out of your training data and your hard earned class label annotations.

27 May 2020 • 2 min read

Introducing An Even Better Way to Preview Image Preprocessing and Augmentation

Knowing what preprocessing and augmentation steps to apply is hard. We've written many individual posts about the steps required to make informed resize decisions (how to resize images in image preprocessing) to random crop augmentation (how to implement random crop augmentation) and many steps in between. Show, Don&

24 May 2020 • 3 min read

Thermal Infrared Dataset for Object Detection

Computer vision is performed on a wide array of imaging data: photographs, screenshots [https://public.roboflow.com/object-detection/website-screenshots], videos [https://blog.roboflow.com/using-video-computer-vision/]. Commonly, this data is captured in similar perception to how humans see – along the visible red, green, and blue (RGB) color spectrum. However, there'

29 Apr 2020 • 3 min read

Breaking Down Roboflow's Health Check Dimension Insights

Roboflow [https://roboflow.ai] 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

28 Apr 2020 • 1 min read

Introducing the Roboflow Model Library

Over the past few months we've been building up a library of easy to use, open source computer vision models [https://models.roboflow.ai/]. We've now given them a home: the Roboflow Model Library [https://models.roboflow.ai]. https://models.roboflow.ai has an overview of

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

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 [https://models.roboflow.com]’s performance -- without needing to collect any more data

27 Jan 2020 • 2 min read

Introducing Public Datasets

One of the most painstaking components of getting started with computer vision is getting access to clean, labeled data. For example, when the Roboflow team built BoardBoss, we painstakingly collected hundreds of Boggle board images from various devices (iPhone 7, iPhone X, iPhone 11…) and then labeled each individual letter