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
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.
Missed the event or looking for the recording? Check out the Roboflow + Paperspace Detectron2 webinar recording here and notebook here!Tomorrow, Roboflow and Paperspace are co-hosting a webinar teaching you
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.
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
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.
We are pretty excited about the Luxonis OpenCV AI Kit (OAK-D) device at Roboflow, and we're not alone. Our excitement has naturally led us to create another tutorial on how to train and deploy a custom object detection model leveraging Roboflow and DepthAI, to the edge, with depth, faster.
Computer Vision (and Machine Learning in general) is one of those fields that can seem hard to approach because there are so many industry-specific words (or common words used in novel ways) that it can feel a bit like you're trying to learn a new language when you're trying to get started.
This guide will take you the long distance from unlabeled images to a working computer vision model deployed and inferencing live at 15FPS on the affordable and scalable Luxonis OpenCV AI Kit (OAK) device.
Impatient? Jump to our VGG-16 Colab notebook. Image classification models discern what a given image contains based on the entirety of an image's content. And while they're consistently getting better,
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.
We're introducing a new experiment this week: Roboflow is launching a Roboflow YouTube channel.We've been encouraged by the popularity of our computer vision tutorials. When Googling for some architectures,
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
The success of your machine learning model starts well before you ever begin training. Ensuring you have representative images, high quality labels, appropriate preprocessing steps, and augmentations to guard against