Ze względu na iteracyjny charakter algorytmów głębokiego uczenia, ich złożoność rośnie wraz z większą liczbą warstw oraz dużymi ilościami danych niezbędnych do uczenia sieci. VIEW VIDEO Kit Kat Segmentation (11:44) This case study describes the Deep Learning workflow used to segment four different phases of a Kit Kat chocolate-covered wafer bar.

W związku z tym, rozwiązywanie problemów z pomocą deep learning wymaga bardzo dużej mocy obliczeniowej.

Deep Learning is included in all versions of Dragonfly as of the 4.0 release… After the trial period has ended, non-commercial users can continue using Deep Learning for … We are currently working on supporting 3D models, multiple inputs and outputs, and other great features that will be included in the next Dragonfly release. Our sales and support teams will be available to answer any questions as you evaluate the Deep Learning tool for Dragonfly. Dragonfly’s Deep Learning solution is bundled with pre-built and pre-trained neural networks, implementing such powerful solutions as UNet, DenseNet, FusionNet and many others.


This is the first version of our Deep Learning tool, and most model limitations will be addressed in future versions of the tool.
In addition, we would be happy to arrange a demo for a more guided overview of the application, as well as discuss workflows and implementation best practices for you and your organization.

Introduction to Deep Learning (44:07) This recorded webinar introduces Dragonfly's Deep Learning tool and includes topics such as how to download and choose a neural network model.

Set apart by Dragonfly's pioneering Deep Learning solution, Dragonfly's segmentation toolkit includes watershed and superpixel methods, 2D histographic segmentation, and other innovative 2D and 3D tools to label features of interest with ease and precision. While You Evaluate Deep Learning. how to retrain a model, and how to share and reuse models. Learn how Dragonfly's Deep Learning tool can be applied directly to your data for such image processing tasks as segmentation, upsampling, and denoising.

Możliwości i zastosowania deep learning.