What is CUDA?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us [

Active Learning Tips: How to Continuously Improve Your Production Model

You've built your first model and plan to get it deployed to production. Now what? Like any software, the computer vision model needs to be continuously improved for

Using Computer Vision to Clean the World's Oceans

Global plastic production has exceeded 500 million tons. Moreover, estimates from the US Environmental Protection Agency []

5 Reasons to not Fully Outsource Labeling

So you're working on building a machine learning model, and you have hit the realization that you will need to annotate a lot of data to build a performant model. In the machine learning meta today, you will be bombarded with services offering to fully outsource your labeling woes.

Solving the Out of Scope Problem

When we are teaching a machine learning model to recognize items of interest, we often take a laser focus towards gathering a dataset that is representative of the task we want our algorithm to master.

How AI Protects My Garden from Rabbits

Rabbits were eating all of my vegetables. I decided to take a stand and implement a computer vision enabled system to automatically spook them away from my garden.

How Atos Uses Computer Vision to Monitor Office Occupancy

Building & deploying a privacy-first model to the edge with Roboflow in 60 days The COVID-19 pandemic changed how and where we work. Fortunately, in some countries, the pandemic appears

How Your Favorite Brands Are Using Computer Vision

If you’ve ever tried to explain how computer vision works to your friends, family or colleagues, you probably know that it can be hard to do. This is especially

How to Train YOLOR on a Custom Dataset

The YOLO family recently got a new champion - YOLOR: You Only Learn One Representation. In this post, we will walk through how you can train YOLOR to recognize object detection data for your custom use case.

Choosing the Right Problem Statement

Creating a computer vision model, at the outset, seems like a pretty involved task. Even if you’re using an end-to-end solution [] like Roboflow, the

An Introduction to ImageNet

The ImageNet dataset is long-standing landmark in computer vision. The impact ImageNet has had on computer vision research is driven by the dataset's size and semantic diversity. Let&

What is the JAX Deep Learning Framework?

You've probably heard of TensorFlow and PyTorch, and maybe you've even heard of MXNet - but there is a new kid on the block of machine learning frameworks - Google's JAX.

For the People, By the People

Computer vision, on the whole, is an ambitious undertaking. We are developing technology that can see the world as we see it - to recognize simple objects like trees and

How to Train MobileNetV2 On a Custom Dataset

In this post, we will walk through how you can train MobileNetV2 to recognize image classification data for your custom use case.

Building vs. Buying a Computer Vision Platform

“You could do what Roboflow does yourself but…why would you?” -Jack Clark, Co-Founder of Anthropic [], former Policy Directory at OpenAI [], It’s

How to Train with Microsoft Azure Custom Vision and Roboflow

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 [

Your Comprehensive Guide to the YOLO Family of Models

YOLO (You Only Look Once) is a family of computer vision models that has gained significant fanfare since Joseph Redmon, Santosh Divvala,  Ross Girshick, and Ali Farhadi introduced the novel

What Does "End to End" Really Mean?

Developing, deploying and optimizing computer vision models used to be a cumbersome, painful process. With Roboflow, we sought to democratize this technology, which (first and foremost) meant knocking down the

Prompt Engineering: The Magic Words to using OpenAI's CLIP

Featuring rock, paper, scissors. OpenAI's CLIP model [] (Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier that leverages knowledge of the English language

License Plate Detection and OCR using Roboflow Inference API

In this post, we’ll walk you through creating a license plate detection and OCR model using Roboflow that you can programmatically use for your own projects.

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

Image Augmentations for Aerial Datasets

When creating computer vision models, data augmentation [] can improve model performance with an existing image dataset. Image augmentation increases the size and variability of

How important is subject similarity for transfer learning?

Using transfer learning [] to initialize your computer vision model from pre-trained weights rather than starting from scratch (initializing randomly) has been shown to increase performance

Our Favorite Computer Vision Courses

A question we frequently receive at Roboflow is, "What is the best class for learning computer vision?" Like most questions, the answer does depend on your background and

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