tiny-dnn

Why we included this project

tiny-dnn is a deep learning framework written in C++14 that ships as a single header with no dependencies, which makes it workable in places where a heavyweight framework with native binaries and runtime libraries is overkill. You add one header to the build and get the usual building blocks, convolutional and pooling layers for feature extraction, fully connected layers, batch normalization, and optimizers such as Adam and RMSprop, and it can import Caffe models as well. That simplicity makes it a plausible fit for embedded systems and IoT devices where memory and compute are tight. The tradeoff is upkeep: development has been effectively dormant since 2022 and the maintainers have said they are looking to pass the project on, so plan for a self-contained library you run as-is rather than a dependency expecting regular upstream fixes.