State of the Art Neural Networks for Deep Learning

Overview

pyradox

This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2


Installation

pip install git+https://github.com/Ritvik19/pyradox.git

Usage

Modules

Module Description Input Shape Output Shape Usage
Rescale A layer that rescales the input: x_out = (x_in -mu) / sigma Arbitrary Same shape as input check here
Convolution 2D Applies 2D Convolution followed by Batch Normalization (optional) and Dropout (optional) 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Densely Connected Densely Connected Layer followed by Batch Normalization (optional) and Dropout (optional) 2D tensor with shape (batch_size, input_dim) 2D tensor with shape (batch_size, n_units) check here
DenseNet Convolution Block A Convolution block for DenseNets 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
DenseNet Convolution Block A Convolution block for DenseNets 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
DenseNet Transition Block A Transition block for DenseNets 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Dense Skip Connection Implementation of a skip connection for densely connected layer 2D tensor with shape (batch_size, input_dim) 2D tensor with shape (batch_size, n_units) check here
VGG Module Implementation of VGG Modules with slight modifications, Applies multiple 2D Convolution followed by Batch Normalization (optional), Dropout (optional) and MaxPooling 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inception Conv Implementation of 2D Convolution Layer for Inception Net, Convolution Layer followed by Batch Normalization, Activation and optional Dropout 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inception Block Implementation on Inception Mixing Block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Xception Block A customised implementation of Xception Block (Depthwise Separable Convolutions) 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net Block Implementation of Efficient Net Block (Depthwise Separable Convolutions) 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Conv Skip Connection Implementation of Skip Connection for Convolution Layer 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net Block Customized Implementation of ResNet Block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net V2 Block Customized Implementation of ResNetV2 Block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res NeXt Block Customized Implementation of ResNeXt Block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inception Res Net Conv 2D Implementation of Convolution Layer for Inception Res Net: Convolution2d followed by Batch Norm 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inception Res Net Block Implementation of Inception-ResNet block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) block 8 Block 17 Block 35
NAS Net Separable Conv Block Adds 2 blocks of Separable Conv Batch Norm 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
NAS Net Adjust Block Adjusts the input previous path to match the shape of the input
NAS Net Normal A Cell Normal cell for NASNet-A
NAS Net Reduction A Cell Reduction cell for NASNet-A
Mobile Net Conv Block Adds an initial convolution layer with batch normalization and activation 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Mobile Net Depth Wise Conv Block Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, activation, pointwise convolution, batch normalization and activation 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inverted Res Block Adds an Inverted ResNet block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
SEBlock Adds a Squeeze Excite Block 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here

ConvNets

Module Description Input Shape Output Shape Usage
Generalized Dense Nets A generalization of Densely Connected Convolutional Networks (Dense Nets) 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Densely Connected Convolutional Network 121 A modified implementation of Densely Connected Convolutional Network 121 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Densely Connected Convolutional Network 169 A modified implementation of Densely Connected Convolutional Network 169 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Densely Connected Convolutional Network 201 A modified implementation of Densely Connected Convolutional Network 201 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Generalized VGG A generalization of VGG network 4D tensor with shape (batch_shape, rows, cols, channels) 4D or 2D tensor usage 1 usage 2
VGG 16 A modified implementation of VGG16 network 4D tensor with shape (batch_shape, rows, cols, channels) 2D tensor with shape (batch_shape, new_dim) usage 1 usage 2
VGG 19 A modified implementation of VGG19 network 4D tensor with shape (batch_shape, rows, cols, channels) 2D tensor with shape (batch_shape, new_dim) usage 1 usage 2
Inception V3 Customized Implementation of Inception Net 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Generalized Xception Generalized Implementation of XceptionNet (Depthwise Separable Convolutions) 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Xception Net A Customised Implementation of XceptionNet 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net Generalized Implementation of Effiecient Net 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B0 Customized Implementation of Efficient Net B0 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B1 Customized Implementation of Efficient Net B1 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B2 Customized Implementation of Efficient Net B2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B3 Customized Implementation of Efficient Net B3 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B4 Customized Implementation of Efficient Net B4 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B5 Customized Implementation of Efficient Net B5 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B6 Customized Implementation of Efficient Net B6 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Efficient Net B7 Customized Implementation of Efficient Net B7 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net Customized Implementation of Res Net 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 50 Customized Implementation of Res Net 50 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 101 Customized Implementation of Res Net 101 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 152 Customized Implementation of Res Net 152 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net V2 Customized Implementation of Res Net V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 50 V2 Customized Implementation of Res Net 50 V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 101 V2 Customized Implementation of Res Net 101 V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res Net 152 V2 Customized Implementation of Res Net 152 V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res NeXt Customized Implementation of Res NeXt 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res NeXt 50 Customized Implementation of Res NeXt 50 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res NeXt 101 Customized Implementation of Res NeXt 101 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Res NeXt 152 Customized Implementation of Res NeXt 152 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
Inception Res Net V2 Customized Implementation of Inception Res Net V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
NAS Net Generalised Implementation of NAS Net 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
NAS Net Mobile Customized Implementation of NAS Net Mobile 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
NAS Net Large Customized Implementation of NAS Net Large 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) check here
MobileNet Customized Implementation of MobileNet 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) usage 1 usage 2
Mobile Net V2 Customized Implementation of Mobile Net V2 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) usage 1 usage 2
Mobile Net V3 Customized Implementation of Mobile Net V3 4D tensor with shape (batch_shape, rows, cols, channels) 4D tensor with shape (batch_shape, new_rows, new_cols, new_channels) usage 1 usage 2

DenseNets

Module Description Input Shape Output Shape Usage
Densely Connected Network Network of Densely Connected Layers followed by Batch Normalization (optional) and Dropout (optional) 2D tensor with shape (batch_size, input_dim) 2D tensor with shape (batch_size, new_dim) check here
Densely Connected Resnet Network of skip connections for densely connected layer 2D tensor with shape (batch_size, input_dim) 2D tensor with shape (batch_size, new_dim) check here
You might also like...
State-of-the-art data augmentation search algorithms in PyTorch
State-of-the-art data augmentation search algorithms in PyTorch

MuarAugment Description MuarAugment is a package providing the easiest way to a state-of-the-art data augmentation pipeline. How to use You can instal

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A selection of State Of The Art research papers (and code) on human trajectory prediction (forecasting). Papers marked with [W] are workshop papers.

A state of the art of new lightweight YOLO model implemented by TensorFlow 2.
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time. FastReID is a research platform that implements state-of-the-art re-identification algorithms.
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+

PaddlePaddle Vision Transformers State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 🤖 PaddlePaddle Visual Transformers (PaddleViT or

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

Comments
Releases(v1.0.1)
Owner
Ritvik Rastogi
I have been writing code since 2016, and taught myself a handful of skills and programming languages. I love solving problems by writing code
Ritvik Rastogi
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
Neighborhood Reconstructing Autoencoders

Neighborhood Reconstructing Autoencoders The official repository for Neighborhood Reconstructing Autoencoders (Lee, Kwon, and Park, NeurIPS 2021). T

Yonghyeon Lee 24 Dec 14, 2022
An example showing how to use jax to train resnet50 on multi-node multi-GPU

jax-multi-gpu-resnet50-example This repo shows how to use jax for multi-node multi-GPU training. The example is adapted from the resnet50 example in d

Yangzihao Wang 20 Jul 04, 2022
Everything's Talkin': Pareidolia Face Reenactment (CVPR2021)

Everything's Talkin': Pareidolia Face Reenactment (CVPR2021) Linsen Song, Wayne Wu, Chaoyou Fu, Chen Qian, Chen Change Loy, and Ran He [Paper], [Video

71 Dec 21, 2022
An open source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+. Including offline map and navigation.

Pi Zero Bikecomputer An open-source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+ https://github.com/hishizuka/pizero_bikecompute

hishizuka 264 Jan 02, 2023
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Code

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Requirements Python 3.8 or later with all requirements.txt dependencies installed,

88 Dec 12, 2022
A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud.

Lidar with Velocity A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud. related paper: Lidar with Velocity : Motion

ISEE Research Group 164 Dec 30, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language This repository contains UA-GEC data and an accompanying Python lib

Grammarly 226 Dec 29, 2022
This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

Zhong Peixiang 35 Nov 17, 2022
sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

445 Jan 02, 2023
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and i

yifan liu 147 Dec 03, 2022
[2021][ICCV][FSNet] Full-Duplex Strategy for Video Object Segmentation

Full-Duplex Strategy for Video Object Segmentation (ICCV, 2021) Authors: Ge-Peng Ji, Keren Fu, Zhe Wu, Deng-Ping Fan*, Jianbing Shen, & Ling Shao This

Daniel-Ji 55 Dec 22, 2022
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

Edson-Niu 60 Nov 29, 2022
Facebook AI Image Similarity Challenge: Descriptor Track

Facebook AI Image Similarity Challenge: Descriptor Track This repository contains the code for our solution to the Facebook AI Image Similarity Challe

Sergio MP 17 Dec 14, 2022
Projects for AI/ML and IoT integration for games and other presented at re:Invent 2021.

Playground4AWS Projects for AI/ML and IoT integration for games and other presented at re:Invent 2021. Architecture Minecraft and Lamps This project i

Vinicius Senger 5 Nov 30, 2022
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer This is the official pytorch implementation of the CoTr: Paper: CoTr: Ef

218 Dec 25, 2022
i-RevNet Pytorch Code

i-RevNet: Deep Invertible Networks Pytorch implementation of i-RevNets. i-RevNets define a family of fully invertible deep networks, built from a succ

Jörn Jacobsen 378 Dec 06, 2022