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
We're proud to share that Roboflow has entered into a partnership agreement with Ultralytics, the creators of YOLOv5, and that Roboflow is now the official dataset management and annotation tool
We're ecstatic to announce the launch of our newest pip package, a way for you to natively interface with your Roboflow workspace through your Python scripts and Jupyter notebooks! Read
Today we're excited to announce the launch of our new REST API, a way for your code to interface with the information stored in your Roboflow workspace. Read the full
Welcome to another installment of the Roboflow Changelog where we summarize the improvements to Roboflow's product suite over the preceding month. You can find the last changelog (for August 2021)
We are exciting to announce that you can now track objects frame over frame in video and camera stream using the Roboflow Inference API and the open source zero shot object tracking repository, without having to train a separate classifier for your object track features.
Each month we share a recap of our monthly updates and releases in the Roboflow Changelog. The previous Changelog is available here. In July, the product team's major emphasis was
We've seen tremendous interest in Roboflow from the research community. Faculty and students from institutions ranging from California to Malta to Taiwan have been using Roboflow to accelerate their computer vision work.
With Roboflow, you can train a custom computer vision model in one click and get an infinitely scalable API to receive predictions and you can also deploy directly to devices
We are excited to announce full support for image classification in Roboflow, from image collection and organization, to annotation, to custom training, and deployment.
Welcome to this month's installment of the Roboflow Changelog highlighting all the updates we've pushed in the past month. The update for last month can be found here. In June
When it launched in early 2020, Roboflow was primarily a tool for converting object detection datasets. As the months have gone on we have expanded its capabilities to include collaboration,
Roboflow is a tool for building robust machine learning operations pipelines for computer vision: from collecting and organizing images, annotating, training, deploying, and creating active learning pipelines to rapidly create
Each month, we publish a list of recent features and additions to the Roboflow suite of products. The previous month's update is here. In May there has been a bevy
Deploying computer vision models to the edge is critical to unlocking new use cases like in places with limited internet connectivity or where minimal latency is essential. That might be
The changelog is our compendium of monthly updates. If you want to take a walk down memory lane, you can being your trip back in time with last month's edition.
When you log into your Roboflow account you'll notice a fresh look, but the features you know and love are all still just a click away. We've reorganized the app
IBM recently announced they are shutting down IBM Visual Inspection, their product for creating custom computer vision models for classification and object detection. No new instances can be created and
Each month we bring you the high level bullet-points of improvements and additions to Roboflow. If you missed it, last month's changelog is here. In March we were focused on
Deploy Models to NVIDIA Embedded Devices Deploying models to the edge offers unique benefits: inference speeds can be increased, the model can run offline, and data can be processed locally.
