Roboflow supports tiling during training as a pre-processing step to train
models to detect small objects in large images
[https://blog.roboflow.com/detect-small-objects/], and now you can also use
Polygons have traditionally been used for training image segmentation models, but they can also improve the training of object detection models. Object detection models are typically much faster and more widely supported, so they're still the best choice for solving many problems.
Roboflow Annotate [https://docs.roboflow.com/annotate] has been used to label
over 66 million images and as teams collaborate to manage large datasets
[https://blog.roboflow.com/annotation-workflow/], it’
Constrained bandwith? Left without the option to extend production data beyond your local network or corporate firewall? In need of real-time inference speeds on the edge? The Roboflow Mobile iOS SDK is a great option if you are developing an iOS application.
90,000 Datasets and 7,000 Pre-trained Models Available
Roboflow Universe [https://universe.roboflow.com/] launched in August 2021 with
50 open source datasets and opened our computer vision infrastructure
Roboflow Annotate [https://roboflow.com/annotate] has been used to label over 66
million images and teams have been able to label more images with our recent
release of collaborative
Microsoft Azure customers worldwide can now gain access to Roboflow to take
advantage of the end-to-end computer vision platform to give their software the
sense of sight.
We’re pleased
What Is Instance Segmentation?
Instance segmentation
[https://blog.roboflow.com/instance-segmentation-roboflow/], also known as image
segmentation, is the computer vision task of recognizing objects in images along
with their associated
Regardless of whether your project is a new product line, a new industrial production system, a research project, or a personal one to help you learn what computer vision is all about, you'll want to add "pip install roboflow" to your code - and here's why.
We are excited to release support for instance segmentation projects on Roboflow. Instance segmentation allows your computer vision model to know the specific outline of an object in an image, unlocking new use cases for Roboflow in your application.
Spring is finally here and there is computer vision in the air! Each month, we
share a recap of product and company updates; if you missed last month's edition
of
Roboflow has supported the entire process of creating object detection and
single-label classification computer vision projects, from collecting and
annotating images to training and deploying a model since our launch
Over the last year, thousands of custom computer vision models have been trained
with Roboflow Train [https://docs.roboflow.com/train] and millions of inferences
have been made via Roboflow
When training any machine learning model, you must trade off inference speed for accuracy. Larger models with more parameters are uniformly more accurate, and smaller models with fewer parameters are uniformly faster to infer.
Even though it's the shortest month of the year, we accomplished a lot! Each
month, we share a recap of product and company updates; if you missed last
month's edition
It might be cold outside, but Roboflow is bringing the heat to kick off 2022!
Each month, we share a recap of product and company updates; if you missed last
Roboflow Annotate [https://roboflow.com/annotate] has become an essential tool
used by tens of thousands of developers to label images of everything from
planes [https://universe.roboflow.com/skybot-cam/
It's been a whirlwind of a month at Roboflow with lots of changes, updates, and
expansion going on behind the scenes. Each month, we share a recap of product
and
You can now export the bounding boxes from your object detection dataset as
cropped images usable with classification models. This update will enable easily
prototyping two-pass models for use-cases like
Our emphasis this month was on "code tentacles", or improving the ways that
Roboflow integrates with your codebase. Below is a recap of what we launched and
improved. Rewind to
