• 8 min read
How to Train and Deploy Custom Models to Your OAK
In this blog, we'll walk through the Roboflow custom model deployment process to the OAK and show just how seamless it can be.
• 3 min read
Partnering with Luxonis and OpenCV for Seamless Deployment to OpenCV AI Kit
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 deploying to a rural field in agriculture or
computer vision in high throughput manufacturing
[https://blog.roboflow.com/computer-vision-use-cases-for-manufacturing/].
As
• 1 min read
Roboflow Changelog: May 2021
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.
In April, our major product efforts centered around launching our brand new user interface. This redesign of our
• 1 min read
Roboflow's new User Interface
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 to make things easier to find, simpler to navigate, and primed for
future enhancement.
Video overview
• 6 min read
How to Use Roboflow with IBM Visual Recognition (IBM Watson vs Roboflow)
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 all current instances will be
fully shutdown in December 2021. This has left many looking for IBM Visual
Inspection alternatives.
• 1 min read
Roboflow Changelog: April 2021
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 expanding Roboflow's inference capabilities with the release of on-device NVIDIA Jetson support, our open source video
• 2 min read
Video Inference with Roboflow
One common question we get is "Can I use my Roboflow model on a video?" The
answer is yes! Videos are really just a sequence of images, so your model can
give predictions just like it does on images.
The process is simple: 1) split your video into
• 2 min read
Introducing Roboflow Support for NVIDIA Jetson
💡Roboflow Inference, which you can use to deploy computer vision models to a Jetson (among many other devices), is now available as an open source project.
See the Quickstart to get started.
Deploy Models to NVIDIA Embedded Devices
Deploying models to the edge offers unique benefits: inference speeds can be
• 1 min read
Roboflow Changelog: March 2021
The monthly changelog showcases improvements in Roboflow over the past month. You can find the previous changelog here.
In February, we had a major focus on reinforcing our foundation; after several months of rapid feature expansion we spent a considerable amount of time fixing bugs, improving infrastructure, and preparing for
• 3 min read
Introducing Upload Batches, the Unannotated Queue, and Mark as Null
This week we updated the workflow for uploading and annotating images to
streamline the process, help you keep track of your progress, and make it easier
to divide work amongst your team.
If you've worked on an ongoing computer vision project with iterative
improvement via active learning [https:
• 2 min read
Roboflow Changelog: February 2021
Welcome to our monthly roundup post of new features and enhancements. You can find the previous changelog here.
The biggest new features released this month were transfer learning and Label Assist.
Roboflow Organize
* Announced teams for all (now teams of up to 3 people can collaborate on the Starter Plan
• 1 min read
Launch: Model Assisted Labeling
Use Roboflow Trained Models to Annotate Data
One of the most time-consuming parts of the computer vision workflow is curating a high-quality dataset. When we launched Roboflow Annotate last month we aimed to streamline this process by launching an easy to use image annotation tool built right into your computer
• 2 min read
Teams for All: Create Free Teams on Roboflow
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 multiple sources; annotating data with colleagues; sharing
versioned datasets; and using deployed models.
We're pleased to introduce free
• 3 min read
Using the Upload API to Collect Images from the Wild
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 you deploy to the real world you'll invariably find
edge cases that confuse your model.
Collecting
• 2 min read
Introducing the Object Count Histogram
We are excited to announce the introduction of object count histograms, now available in the Roboflow dataset health check.
• 2 min read
Roboflow Changelog: January 2021
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 Annotate, an integrated image annotation tool included in all Roboflow plans. In the first few weeks, over 1500
• 2 min read
Get a Hosted API for your Object Detection Model
Roboflow provides tools for labeling [https://roboflow.com/annotate], organizing
[https://docs.roboflow.com/], and training [https://docs.roboflow.com/train] a
computer vision model [https://models.roboflow.com]. Once you finish running one
of the Jupyter notebooks from our Computer Vision tutorials
[https://blog.roboflow.com/tag/tutorials/] you
• 2 min read
Introducing Roboflow Annotate
Since we launched Roboflow [https://roboflow.com] 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 of their project that are unique, not
on reinventing the wheel. But there's been one
• 1 min read
Share Your Datasets with the Computer Vision Community
Computer vision problems start with finding high quality image datasets.
Fortunately, access to common image data is increasingly easier. Datasets like
Microsoft's COCO dataset [https://blog.roboflow.com/coco-dataset/] and the
Pascal VOC dataset
[https://public.roboflow.com/object-detection/pascal-voc-2012] provide a
standard for common objects and measuring
• 1 min read
Roboflow Changelog: December 2020
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 plan features in-app
* Sped up thumbnail generation
* Added image upload by URL to the upload API
* Paginated the image listing
• 2 min read
Revamping Train, Validation, Test, Split Management
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.
• 3 min read
Hands on with the Roboflow Infer Web Application Interface Builder
After you train a model with Roboflow Train [https://docs.roboflow.com/train],
you're provided with three immediate ways to use your model: a curl command, the
direct URL, and an Example Web App. In this post, we'll demonstrate hands on how
to use the web
• 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@
• 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
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