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
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
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
Andrew Ng [https://twitter.com/andrewyng], the co-founder of Google Brain
[https://research.google/teams/brain/] and Coursera [https://www.coursera.org/]
and former Chief Scientist at Baidu [http://research.
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
Paint.wtf [https://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
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
OpenCV [https://opencv.org/about/] has been a key part of advancing computer
vision capabilities for developers for over 20 years. The Open Source Computer
Vision Library on Github [https:
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
This is a guest post written by Ethan Arsht and Raluca Cîrju.
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Google Earth Engine [https://earthengine.google.com/] is a powerful tool for
analyzing and acquiring geographic data.
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.
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 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.
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.
The below post is a guest post written by data scientist Joseph Rosenblum
[https://www.linkedin.com/in/joseph-rosenblum/]. He is using computer vision to
make cities more efficient and
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
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
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,
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.
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
In Bedford–Stuyvesant, Brooklyn
[https://en.wikipedia.org/wiki/Bedford%E2%80%93Stuyvesant,_Brooklyn] (BedStuy),
Yuri Fukuda regularly walks by a mural that showcases prominent female leaders.
Since October 2005,
