Roboflow and GPT-4 will be even more powerful when used in conjunction. In this post we preview some of the new features that will be coming to Roboflow in the coming weeks.
OpenAI released GPT-4 showcasing strong multi-modal general AI capabilities in addition to impressive logical reasoning capability. Are general models going to obviate the need to label images and train models?
The field of computer vision advances with the newest release of YOLOv8, setting a new state of the art for object detection and instance segmentation.
When you are training machine learning models, it is essential to pick hardware that optimizes your models performance relative to cost. In training, the name of the game is speed per epoch – how fast can your hardware run the calculations it needs to train your model on your data.
Roboflow 100 (RF100) is a crowdsourced object detection benchmark. The dataset consists of 100 datasets, 7 imagery domains, 224,714 images, and 829 class labels with over 11,170 labeling hours.
In this post, we take the opportunity to reflect on the computer vision research landscape at CVPR 2022 and highlight our favorite research papers and themes.
In this post, we showcase training and deploying YOLOS end to end, from labeling your data, to training your model, to deploying your model on AWS for inference.
We are excited to release support for instance segmentation projects on Roboflow. Instance segmentation allows your computer vision model to know the specific outline of an object in an image, unlocking new use cases for Roboflow in your application.
Learn how to annotate images in CVAT, an open-source, web-based tool for labeling data for object detection, segmentation, classification, and other tasks.
Over the last year, thousands of custom computer vision models have been trained
with Roboflow Train [https://docs.roboflow.com/train] and millions of inferences
have been made via Roboflow
When training any machine learning model, you must trade off inference speed for accuracy. Larger models with more parameters are uniformly more accurate, and smaller models with fewer parameters are uniformly faster to infer.
Recently, AWS released SageMaker Studio Lab, its competitor service to Google Colab. I dove into comparing Google Colab to Studio Lab and here is what I found.
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.
