Neural-Network-Architecture-Diagrams

Why we included this project

Reading a research paper is much easier when the network's flow is drawn out, and this repo is a practical gallery of exactly those drawings. It collects editable diagrams.net (draw.io) renderings of well-known architectures, including VGG-16, U-Net, YOLO v1, autoencoders, RNNs, and feature pyramid networks, each contributed by a different modeler. The real value is that you can study how experienced practitioners break a complex topology into readable blocks, then reuse the draw.io files as a starting point for documenting your own models. Students prepping for interviews or presentations and engineers who need clean visuals for papers, slides, or docs will find a template that fits. Contributor credits also link back to the people who drew each diagram, which is handy if you want to see the reasoning behind a particular layout.