The article below was contributed by Timothy Malche, an assistant professor in the Department of Computer Applications at Manipal University Jaipur.
Pill Inspection System Overview
This project creates a system
Counting moving objects is one of the most popular use cases in computer vision. It is used, among other things, in traffic analysis and as part of the automation of manufacturing processes. That is why understanding how to do it well is crucial for any CV engineer.
When you are training machine learning models, it is essential to pick hardware that optimizes your models performance relative to cost. In training, the name of the game is speed per epoch – how fast can your hardware run the calculations it needs to train your model on your data.
This is a guest post by Kristen Kehrer
, Developer Advocate at CometML [https://www.comet.com/site/]. Since
According to Gartner
, 85% of machine learning projects fail. Worse yet, Gartner predicts that this
trend will continue through 2022. So, when
Roboflow has extensive deployment options [https://roboflow.com/deploy] for
getting your model into production. But, sometimes, you just want to get
something simple running on your development machine.
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!
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.