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.

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 no secret that building a computer vision

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 pipelines to rapidly create

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

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.

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 to classify images without having to be trained

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.

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.

The power of image augmentation: an experiment

One of the amazing things about computer vision is using existing images plus random changes to increase your effective sample size. Suppose you have one photo containing a coffee mug.

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 and decrease training time. It

Zero-Shot Content Moderation with OpenAI's New CLIP Model

When creating a platform on which people can create and share content, there’s often a question of content moderation. Content moderation can mean a whole host of different things,

What is Embedded Machine Learning?

Machine learning – the software discipline of mapping inputs to outputs without explicitly programmed relationships – requires substantial computational resources. Traditionally, this limits where machine learning models can run to very powerful

ELI5 CLIP: A Beginner's Guide to the CLIP Model

You may have heard about OpenAI's CLIP model. If you looked it up, you read that CLIP stands for "Contrastive Language-Image Pre-training." That doesn't immediately make much sense to me,

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.

Using Computer Vision to Win at Duck Hunt

Can we use object detection to automate identifying moving objects on a screen? Abhinav Mandava leverages Roboflow to create an aimbot (which automates aiming and firing for the player) for Duck Hunt.

A Primer on Transfer Learning

Teaching Friends to Skateboard 🛹Imagine you have two friends that you're trying to teach how to skateboard. Both have never skateboarded previously. Friend A, call them Anna, has snowboarded in

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

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

Football, Kaggle, Roboflow: Using Computer Vision to Tackle Helmet Safety

If you're searching for a dataset to use or are looking to improve your data science modeling skills, Kaggle is a great resource for free data and for competitions. For

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.

Apple's M1 is up to 3.6x as fast at training machine learning models

We compared the Apple M1 chip to the Intel Core i5 chip on an object detection task using Create ML.

5 Strategies for Handling Unbalanced Classes

Suppose you're trying to teach an alien – like one of the crewmates from the wildly popular game Among Us – to tell the difference between a human and a dog. "Purp

Scaled-YOLOv4 is Now the Best Model for Object Detection

(based on Microsoft COCO benchmarks) The object detection space remains white hot with the recent publication of Scaled-YOLOv4, establishing a new state of the art in object detection. Looking to

What is Active Learning?

Machine learning algorithms are exceptionally data-hungry, requiring thousands – if not millions – of examples to make informed decisions. Providing high quality training data for our algorithms to learn is an expensive

Google Researchers Say Underspecification is Ruining Your Model Performance. Here's Five Ways to Fix That.

We read that Google underspecification paper so you don't have to.

YOLOv4 - Ten Tactics to Build a Better Model

The YOLO v4 repository is currently one of the best places to train a custom object detector, and the capabilities of the Darknet repository are vast. In this post, we discuss and implement ten advanced tactics in YOLO v4 so you can build the best object detection model from your custom dataset.