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 [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
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
For almost a year, Roboflow (our computer vision dataset management tool
[https://roboflow.com]) has lived at roboflow.ai [https://roboflow.ai]. It's
served us well but we
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 [https://arxiv.org/abs/2005.04757] there is still
plenty of room to grow model performance through augmenting our
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
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.
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
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
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 [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.
Knowing how an image preprocessing [https://blog.roboflow.com/tag/preprocessing/] step or
augmentation [https://blog.roboflow.com/tag/augmentation/] is going to appear before you
write the code for
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
One of the most painstaking components of getting started with computer vision
[https://blog.roboflow.com/getting-started-with-roboflow/] is getting access to clean,
labeled data. For example, when the Roboflow [https:
