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,
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.
When evaluating an object detection model in computer vision, mean average
precision [https://blog.roboflow.com/mean-average-precision/] is the most
commonly cited metric for assessing performance. Remember, mean average
precision
Detectron2 is Facebook's open source library for implementing state-of-the-art
computer vision techniques in PyTorch. Facebook introduced Detectron2
[https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/]
in October 2019 as a complete rewrite
The below post is by Kasim Rafiq [https://twitter.com/Kasim21], a
conservationist, Fulbright Scholar, and National Geographic Explorer studying at
UC Santa Cruz. Kasim holds a PhD in Wildlife
In order to ensure our models are generalizing well (rather than memorizing
training data), it is best practice to create a train, test split.
[https://blog.roboflow.com/train-test-split/] That
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.
During the summer of 2019, I received a Facebook message from Roboflow
co-founder Brad Dwyer [https://twitter.com/braddwyer] asking me if I wanted to
design a new mobile app
Google Colab [https://colab.research.google.com/] is Google's hosted Jupyter
Notebook product that provides a free compute environment, including GPU and TPU
[https://blog.roboflow.com/glossary/].
Colab comes
The below post is a lightly edited guest post by David Lee, a data scientist
using computer vision to boost tech accessibility for communities that need it.
David has open
Today, we introduce a new and improved shear augmentation. We'll walk through some details on the change, as well as some intuition and results backing up our reasoning.
Creating successful computer vision models requires handling an ever growing set
of edge cases.
At the 2020 Conference on Computer Vision and Pattern Recognition (CVPR),
Tesla's Senior Director of AI,
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.
In this post, we will demystify the label map by discussing the role that it plays in the computer vision annotation process. Then we will get hands on with some real life examples using a label map.
In this post, we will walk through how to jumpstart your image annotation process using LabelMe, a free, open source labeling tool.
To get started with LabelMe, we will walk
If you're wondering this, you're not alone. The annotation group is the category
that encompasses all of the classes in your dataset. It answers the question
"What kind of things
Recently, Roboflow machine learning engineer Jacob Solawetz
[http://jacobsolawetz.com] sat down with Elisha Odemakinde, an ML researcher and
Community Manager at Data Science Nigeria, for a Fireside chat.
During
At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model.
Can a computer tell the difference between a dandelion and a daisy? In this post
we put these philosophical musings aside, and dive into the the code necessary
to find
Fastai, the popular deep learning framework and MOOC releases fastai v2 with new
improvements to the fastai library, a new online machine learning course, and
new helper repositories.
fastai's layered
