Education

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 a dataset, thereby improving model generalizability.

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

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 what you're

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

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,

Andrew Ng: "Deploying to production means you're halfway there."

Andrew Ng, the co-founder of Google Brain and Coursera and former Chief Scientist at Baidu, spoke at this week's Scale Transform conference on the transition from "big data" to "good

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

How We Built Paint.wtf, an AI Game with 150,000+ Submissions that Judges Your Art

Paint.wtf is an online game that uses AI to score user-submitted digital drawings to zany prompts like, "Draw a giraffe in the arctic" or "Draw a bumblebee loves capitalism.

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,

Roboflow and OpenCV Partner to Advance Computer Vision Capabilities for All Developers

OpenCV has been a key part of advancing computer vision capabilities for developers for over 20 years. The Open Source Computer Vision Library on Github has over 50,000 stars,

How to Use Roboflow and Streamlit to Visualize Object Detection Output

Building an app for blood cell count detection.The app in action.Most technology is designed to make your life, or your work, easier. If your work involves building computer

How to Use Google Earth Engine and Python API to Export Images to Roboflow

This is a guest post written by Ethan Arsht and Raluca Cîrju. Google Earth Engine is a powerful tool for analyzing and acquiring geographic data. Machine learning experts use Google

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.

What is Object Detection? The Ultimate Guide.

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.

A Primer on Transfer Learning

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other related problems. It’s very similar to

Using Computer Vision to Boost Cities' Efficiency by Reallocating Police Resources

The below post is a guest post written by data scientist Joseph Rosenblum. He is using computer vision to make cities more efficient and decrease the bias in traffic-related policing.

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

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

How to Run Jupyter Notebooks on an Apple M1 Mac

You've probably heard a lot about the MacBook that contains the new Apple M1 chip. Quick summary: It's fast. Like, really fast. You, a data scientist or related tech professional,

How to Label Images for Computer Vision Models

This guide walks through tactics to ensure your dataset is as high quality as possible for computer vision tasks.

What is Computer Vision and Machine Vision? A Guide for Beginners

After reading this post, you should have a good understanding of computer vision without a strong technical background and you should know the steps needed to solve a computer vision problem.

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