• 6 min read
Zero-Shot Content Moderation with OpenAI's New CLIP Model
Learn how to use the CLIP zero-shot model to moderate visual content.
• 4 min read
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 supercomputers. But this is changing.
Computation is required at two core moments in the machine learning development lifecycle: model training
• 8 min read
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." It's Cards Against Humanity meets Microsoft Paint.
Paint.wtf became an internet sensation. In
• 4 min read
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.
• 7 min read
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.
• 3 min read
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.
• 4 min read
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 the concept of reusing code in computer programming, except instead of sharing code between different applications or software projects, we’
• 5 min read
How to Try CLIP: OpenAI's Zero-Shot Image Classifier
Earlier this week, OpenAI dropped a bomb on the computer vision world.
• 5 min read
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 example, there's currently an open Kaggle competition detecting American football helmet impacts (crashes) in which you
• 5 min read
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.
• 4 min read
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.
• 8 min read
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 train a Scaled-YOLOv4 model?
Skip this post and jump straight to our Scaled-YOLOv4 training tutorial. You'll have a
• 6 min read
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.
• 5 min read
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.
• 2 min read
Train Test Split Guide and Overview
In order to ensure our models are generalizing well (rather than memorizing training data), it is best practice to create a train, test split. That is, absent rigor, our models can easily overfit to a small subset of examples we've collected. Look no further than Tesla using computer
• 5 min read
How to Save and Load Model Weights in Google Colab
Google Colab is Google's hosted Jupyter Notebook product that provides a free compute environment, including GPU and TPU.
Colab comes "batteries included" with many popular Python packages installed, making it a choice tool for easy model experimentation. For this reason, the Roboflow Model Library includes many
• 8 min read
How to Train a Custom Mobile Object Detection Model (with YOLOv4 Tiny and TensorFlow Lite)
Learn how to train a custom mobile object detection model with YOLOv4 tiny and TensorFlow Lite.
• 5 min read
How to Train a Custom TensorFlow Lite Object Detection Model
Learn how to train a custom TensorFlow Lite object detection model with a custom dataset.
• 9 min read
How to Train Detectron2 on Custom Object Detection Data
Learn how to train a Detectron2 model on a custom object detection dataset.
• 10 min read
How to Train a YOLOv5 Model On a Custom Dataset
Learn how to train a YOLOv5 model on a custom dataset.
• 9 min read
How to Train YOLOv4 on a Custom Dataset
Learn how to train a YOLOv4 model on a custom dataset.
• 7 min read
Data Augmentation in YOLOv4
Learn how data augmentation is used in training YOLOv4 computer vision models.
• 12 min read
How to Create a Synthetic Dataset for Computer Vision
The appeals of synthetic data are alluring: you can rapidly generate a vast amount of diverse, perfectly labeled images for very little cost and without ever leaving the comfort of your office. The good news is: it's easy to try! And we're about to show you how.
• 6 min read
How to Create to a TFRecord File for Computer Vision and Object Detection
TensorFlow expedites the machine learning process markedly. From abstracting complex linear algebra to including pre-trained models and weights, getting the most out of TensorFlow is a full-time job.
However, when it comes to loading data in ways that TensorFlow expects in order to perform as efficiently as it does, every
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