Education

OpenAI's CLIP is the most important advancement in computer vision this year

CLIP is a gigantic leap forward, bringing many of the recent developments from the realm of natural language processing into the mainstream of computer vision: unsupervised learning, transformers, and multimodality

What is TensorRT?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is TensorRT (in 60 Seconds or Fewer)?TensorRT

What is Amazon Rekognition?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is Amazon Rekognition (in 60 Seconds or Fewer)

What is AutoML?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is AutoML (in 60 Seconds or Fewer)?AutoML,

Mitigating the Collision of Apple's CSAM NeuralHash

This morning, Hacker News was ablaze with several stories about vulnerabilities in Apple's CSAM NeuralHash algorithm. Researchers had found a way to convert the NeuralHash model to ONNX format and

What is CoreML?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is CoreML (in 60 Seconds or Fewer)?CoreML

What is OpenVINO?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is OpenVINO (in 60 Seconds or Fewer)?OpenVINO

What is ONNX?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is ONNX (in 60 Seconds or Fewer)?ONNX

What is CUDA?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us. What is CUDA (in 60 Seconds or Fewer)?CUDA

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 potential edge

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 indicate that 30 percent of all produced plastic will end up in the oceans.

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 process will always require you

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.

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 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