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
AWS provides a Python SDK, "Boto3" ,which can be used to access the AWS-account from the local.

Boto3 - The AWS SDK for Python Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python, which allows Python developers to wri

Shreyas Srivastava 1 Oct 25, 2021
Distance-Ratio-Based Formulation for Metric Learning

Distance-Ratio-Based Formulation for Metric Learning Environment Python3 Pytorch (http://pytorch.org/) (version 1.6.0+cu101) json tqdm Preparing datas

Hyeongji Kim 1 Dec 07, 2022
An addernet CUDA version

Training addernet accelerated by CUDA Usage cd adder_cuda python setup.py install cd .. python main.py Environment pytorch 1.10.0 CUDA 11.3 benchmark

LingXY 4 Jun 20, 2022
Code for "Learning to Regrasp by Learning to Place"

Learning2Regrasp Learning to Regrasp by Learning to Place, CoRL 2021. Introduction We propose a point-cloud-based system for robots to predict a seque

Shuo Cheng (成硕) 18 Aug 27, 2022
Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

StochFuzz: A New Solution for Binary-only Fuzzing StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries.

Zhuo Zhang 164 Dec 05, 2022
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion" Coming soon, as soon as I finish a

Ziyao Zeng 14 Feb 26, 2022
Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

[AAAI22] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification We point out the overlooked unbiasedness in long-tailed clas

PatatiPatata 28 Oct 18, 2022
Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

Point-Unet This is an unofficial implementation of the MICCAI 2021 paper Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segment

Namt0d 9 Dec 07, 2022
Official pytorch implementation of the IrwGAN for unaligned image-to-image translation

IrwGAN (ICCV2021) Unaligned Image-to-Image Translation by Learning to Reweight [Update] 12/15/2021 All dataset are released, trained models and genera

37 Nov 09, 2022
Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

R2RNet Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network." Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu

77 Dec 24, 2022
This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs)

Description This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs) in [Gardy et

Ludovic Gardy 0 Feb 09, 2022
This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Tianhong Dai 150 Dec 23, 2022
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

📖 Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) 🔥 If DaGAN is helpful in your photos/projects, please hel

Fa-Ting Hong 503 Jan 04, 2023
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
Efficient and intelligent interactive segmentation annotation software

Efficient and intelligent interactive segmentation annotation software

294 Dec 30, 2022
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

LiDAR fog simulation Created by Martin Hahner at the Computer Vision Lab of ETH Zurich. This is the official code release of the paper Fog Simulation

Martin Hahner 110 Dec 30, 2022
Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

Geometry Processing with Neural Fields Pytorch implementation for the NeurIPS 2021 paper: Geometry Processing with Neural Fields Guandao Yang, Serge B

Guandao Yang 162 Dec 16, 2022
Send text to girlfriend in the morning

Girlfriend Text Send text to girlfriend (or really anyone with a phone number) in the morning 1. Configure your settings in utils.py. phone_number = "

Paras Adhikary 199 Oct 25, 2022
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection

Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection This material is supplementray code for paper accepted in ICDAR 2021 We h

NCSOFT 30 Dec 21, 2022
PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection.

Introduction This repo contains the official PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection. Up

133 Dec 29, 2022