Written by

Jacob Solawetz

Jacob Solawetz

Machine Learning @ Roboflow - building tools and artifacts like this one to help practitioners solve computer vision. I tweet about this stuff with lower latency but higher error rate @JacobSolawetz

What's New in YOLOS?

YOLOS - You Only Look At One Sequence is the newest, and potentially most impactful, iteration on the YOLO family of object detection models.

YOLOv5 v6.0 is here - new Nano model at 1666 FPS

With the v6.0 release, YOLOv5 further solidifies its position as the leading object detection model and repository.

How to Safely Install OpenCV on the Mac M1

Installing OpenCV on the M1 safely is difficult because the M1 operates on an arm64 architecture and most of your python libraries are compiled for amd64. Open this guide to avoid your otherwise inevitable demise.

Making a Handheld Card Counter on the OAK-D-Lite

The portability of the OAK-D-Lite gives us the power to bring computer vision powered solutions anywhere on earth - including your local casino!

How to Implement Object Tracking

This post is a comprehensive guide on how to implement object tracking with your object detection model to track your custom objects

Introducing Zero Shot Object Tracking

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.

Transformers Take Over Object Detection

It seemed just like a matter of time... and now the Transformers neural networks have landed - Microsoft's DyHead achieves state of the art object detection using a Transformer backbone.

How to Train YOLOX On a Custom Dataset

The YOLO family continues to grow with the next model: YOLOX. In this post, we will walk through how you can train YOLOX to recognize object detection data for your custom use case.

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.

Announcing Image Classification Support, End to End

We are excited to announce full support for image classification in Roboflow, from image collection and organization, to annotation, to custom training, and deployment.

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.

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's dive into

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.

The Joys of Sharing Models on OpenCV's Modelplace

If we could all get together and share our model creation and deployments, that would be a very good thing for the computer vision community. Modelplace is a big step in that direction.

License Plate Detection and OCR on an NVIDIA Jetson

In this blog, we discuss how to train and deploy a custom license plate detection model to the NVIDIA Jetson. While we focus on the detection of license plates in particular, this guide also provides an end-to-end guide on deploying custom computer vision models to your NVIDIA Jetson on the edge.

PP-YOLO Strikes Again - Record Object Detection at 68.9FPS

Object detection research is white hot! In the last year alone, we've seen the state of the art reached by YOLOv4, YOLOv5, PP-YOLO, and Scaled-YOLOv4. And now Baidu releases PP-YOLOv2, setting new heights in the object detection space.

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.

Liquid Neural Networks in Computer Vision

Excitement is building in the artificial intelligence community around MIT's recent release of liquid neural networks. The breakthroughs that Hasani and team have made are incredible. In this post, we will discuss the new liquid neural networks and what they might mean for the vision field.

How to Train and Deploy a License Plate Detector to the Luxonis OAK

In this post, we will leverage Roboflow and the Luxonis OAK to train and deploy a custom license plate model to your OAK device.

The Ultimate Guide to Object Detection

Object detection is a computer vision technology that localizes and identifies objects in an image. Due to object detection's versatility, object detection has emerged in the last few years as

Computer Vision Use Cases in Healthcare and Medicine

Computer vision technology continues to expand its use cases in healthcare and medicine. In this post, we will touch on some exciting example use cases for vision in healthcare and medicine and provide some resources on getting started applying vision to these problems.

How to Try CLIP: OpenAI's Zero-Shot Image Classifier

Earlier this week, OpenAI dropped a bomb on the computer vision world.

Introducing the Object Count Histogram

We are excited to announce the introduction of object count histograms, now available in the Roboflow dataset health check.

How to Train Scaled-YOLOv4 to Detect Custom Objects

Object detection technology advances with the release of Scaled-YOLOv4. This blog is written to help you apply Scaled-YOLOv4 to your custom object detection task, to detect any object in the world, given the right training data.