Learning outcomes 💫
By the end of this blog post, you should be able to...
* Understand what RPA is and how it is useful
* Know how to integrate a Power Automate
What is causing the sea lion population to decrease? Is it illegal hunting? Is
it shark and killer whale predation? Or maybe it’s overfishing, causing the sea
lions to
💡Ready to deploy a vision model? Roboflow Inference, the server that powers millions of inferences on the Roboflow platform, is now available as an open source project.
See the Quickstart
Overfitting is when a model fits exactly against its training data. The quality of a model worsens when the machine learning model you trained overfits to training data rather than
Quote of the day:
“Keep calm and carry on.” -Winston Churchill
Computer vision applications can be used in a variety of settings and sometimes
information captured in your dataset might
In this blog post, we are going to introduce autoencoders
[https://en.wikipedia.org/wiki/Autoencoder], describe the several autoencoder
types that exist, and showcase their applications.
An autoencoder is
Tracking the movement of an object has many applications, from tracking robots
in a warehouse to implementing object tracking systems in drones. The basics of
object tracking [https://blog.roboflow.
Computer vision is the among the most compelling technologies of the 21st
century as it has the potential to drive the world's transition to a better
future.
There have been
Computer vision is a diverse field of artificial intelligence that aims to
detect and identify the contents of an image or a video. One of the common
questions that most
You'll hear the words "supervised learning" and "unsupervised learning" a lot in discussions about data science, machine learning, and other related fields. Being able to distinguish between supervised and unsupervised
As Ph.D. students in the Active Robotics Sensing Lab (ARoS) at NC State under the supervision of Dr. Edgar Lobaton, we developed in conjunction with The Engineering Place at NC State a set of activities to walk high school students through the entire computer vision pipeline.
YOLOv5 [https://blog.roboflow.com/yolov5-improvements-and-evaluation/] is one of
the most popular object detection networks in the world, and now object
detection isn't the only trick up its sleeve!
As
NVIDIA's TAO Toolkit provides a framework for fine-tuning popular computer vision models using your own data. In this tutorial, we'll be demonstrating how to use Roboflow to curate a high-quality computer vision dataset to use with NVIDIA's TAO Toolkit.
Everybody hates installing NVIDIA drivers, you have to manually download them,
then install cuda [https://blog.roboflow.com/what-is-cuda/]
[https://search.brave.com/search?q=cuda&source=desktop], be sure
Answering the question "how do I deploy a computer vision model?" can be
difficult. There are so many options. Which one should you choose? How do you
deploy to the
Benefits to Existing Models
Polygons have traditionally been used for training image segmentation models
[https://blog.roboflow.com/instance-segmentation-training-roboflow/], but
polygons can also improve the training of object detection models
TensorFlow Lite [https://www.tensorflow.org/lite], often referred to as TFLite,
is an open source library developed by Google
[https://developers.googleblog.com/2017/11/announcing-tensorflow-lite.html] for
deploying
