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dnn

Deep Neural Network Architectures

This repository contains the definitions for the following architectures, organized by task.

Contents

It contains the definition for the model that started it all.

Papers:

In particular, it contains SqueezeNet-{v1.0,v1.1}.

Papers:

In particular, it contains VGGNet-{11,13,16,19} variants with batch normalization.

Papers:

It contains the definition of the GoogLeNet, also known as InceptionV1.

Papers:

In particular, it contains ResNet-{18,34,50,101,152}-B definitions, in contrast to dlib, which contains the A variants.

Papers:

In particular, it contains DenseNet-{121,169,201,264,161} definitions.

Papers:

In particular, it contains the backbones for DarkNet-19 (introduced in YOLOv1), DarkNet-53 (YOLOv3) and CSPDarknet-53 (YOLOv4).

Papers:

In particular, it contains implementations for VoVNetv2-{19slim,19,27slim,27,39,57,99}, which are very similar to VoVNetv1 (V2 have identiy mapping and effective Squeeze and Excitation on top of V1).

Papers:

In particular, it contains implementations for RepVGG-{A0,A1,A2,B0,B1,B2,B3}.

Note that, at the moment, there is no way to convert from a trained RepVGG model into its inference counterpart. I will investigate how to do that soon.

Papers:

In particular, it contains implementations for YOLOv5{n,s,m,l,x}, which match the ones in ultralytics/yolov5.