Machine learning is a team sport, and getting a computer vision model to production is no exception. All parts of the process are improved with a team: collecting data from
The key to production quality machine learning models is continuous iteration and improvement. The first step is getting a model that is "good enough" for your first version. But once
Welcome to our monthly changelog where we catalog our recent feature additions and improvements. If you missed it, you can find last month's changelog here. This month we launched Roboflow
Roboflow provides tools for labeling, organizing, and training a computer vision model. Once you finish running one of the Jupyter notebooks from our Computer Vision tutorials you can download a
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
Computer vision problems start with high quality image datasets. Fortunately, access to common image data is increasingly easier. Datasets like Microsoft's COCO dataset and the Pascal VOC dataset provide a
Welcome to the first of our monthly changelogs where we will be cataloging our recent feature additions and improvements. Roboflow Organize Added ability to rebalance train/test split Clarified pro
Splitting data into train, validation, and test splits is essential to building good computer vision models. Today, we are announcing in-app changes to Roboflow that make it even easier to manage your train test splits as you are working through the computer vision workflow.
After you train a model with Roboflow Train, you're provided with three immediate ways to use your model: a curl command, the direct URL, and an Example Web App. In
When evaluating an object detection model in computer vision, mean average precision is the most commonly cited metric for assessing performance. Remember, mean average precision is a measure of our
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
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.
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
Keeping track of images and their corresponding annotations is a challenge. Knowing which annotations map to which class, viewing image metadata, and seeing which images correspond to a training, validation,
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
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
Roboflow Pro now supports Cutout and Mosaic. Recent research has shown there is still plenty of room to grow model performance through augmenting our training data. Roboflow has written extensively
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
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.
It's now even easier to scale up projects with Roboflow. We launched Roboflow in January with the mission of democratizing computer vision. Our thesis is simple: you shouldn't need to
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
Computer vision is performed on a wide array of imaging data: photographs, screenshots, videos. Commonly, this data is captured in similar perception to how humans see – along the visible red,
Welcome To Our Office. Come with Questions, Please. The Roboflow team has been inspired and impressed with what our users are building on top of Roboflow. From making models that
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
