dnn
Deep Neural Network Architectures
This repository contains the definitions for the following architectures, organized by task.
Contents
Classification
AlexNet
It contains the definition for the model that started it all.
Papers:
SqueezeNet
In particular, it contains SqueezeNet-{v1.0,v1.1}.
Papers:
VGGNet
In particular, it contains VGGNet-{11,13,16,19} variants with batch normalization.
Papers:
GoogLeNet
It contains the definition of the GoogLeNet, also known as InceptionV1.
Papers:
ResNet
In particular, it contains ResNet-{18,34,50,101,152}-B definitions, in contrast to dlib, which contains the A variants.
Papers:
DenseNet
In particular, it contains DenseNet-{121,169,201,264,161} definitions.
Papers:
DarkNet
In particular, it contains the backbones for DarkNet-19 (introduced in YOLOv1), DarkNet-53 (YOLOv3) and CSPDarknet-53 (YOLOv4).
Papers:
- You Only Look Once: Unified, Real-Time Object Detection
- YOLOv3: An Incremental Improvement
- CSPNet: A New Backbone that can Enhance Learning Capability of CNN
- YOLOv4: Optimal Speed and Accuracy of Object Detection
VoVNet
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:
- An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
- CenterMask: Real-Time Anchor-Free Instance Segmentation
RepVGG
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:
Detection
YOLOv5
In particular, it contains implementations for YOLOv5{n,s,m,l,x}, which match the ones in ultralytics/yolov5.