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.
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.
The YOLO (You Only Look Once) family of models
[https://blog.roboflow.com/guide-to-yolo-models/] continues to grow and right
after YOLOv6 was released, YOLOv7 was delivered quickly after