“You could do what Roboflow does yourself but…why would you?”
-Jack Clark, Co-Founder of Anthropic [https://www.anthropic.com/], former Policy
Directory at OpenAI [https://openai.com/],
It’s
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 [https://blog.roboflow.com/
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
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.
Featuring rock, paper, scissors.
OpenAI's CLIP model [https://models.roboflow.com/classification/clip]
(Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier
that leverages knowledge of the English language to classify
In this post, we’ll walk you through creating a license plate detection and OCR model using Roboflow that you can programmatically use for your own projects.
Object detection research is white hot! In the last year alone, we've seen the state of the art reached by YOLOv4, YOLOv5, PP-YOLO, and Scaled-YOLOv4. And now Baidu releases PP-YOLOv2, setting new heights in the object detection space.
One of the amazing things about computer vision is using existing images plus
random changes to increase your effective sample size. Suppose you have one
photo containing a coffee mug.
Using transfer learning
[https://blog.roboflow.com/a-primer-on-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
When creating a platform on which people can create and share content, there’s
often a question of content moderation
[https://besedo.com/resources/blog/what-is-content-moderation/]. Content
moderation can mean
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
You may have heard about OpenAI's CLIP model [https://openai.com/blog/clip/]. If
you looked it up, you read that CLIP stands for "Contrastive Language-Image
Pre-training." That doesn't immediately
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.
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.
Transfer learning [https://blog.roboflow.com/what-is-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.
If you're searching for a dataset to use or are looking to improve your data
science modeling skills, Kaggle [https://www.kaggle.com/] is a great resource
for free data
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.
Suppose you're trying to teach an alien – like one of the crewmates from the
wildly popular game Among Us [http://www.innersloth.com/gameAmongUs.php] – to
tell the difference between
(based on Microsoft COCO benchmarks)
The object detection space remains white hot with the recent publication of
Scaled-YOLOv4 [https://arxiv.org/abs/2011.08036], establishing a new state of
the
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
