State of the Art Neural Networks for Deep Learning

Related tags

Deep Learningdeep-neural-networksdeep-learningpython3recurrent-neural-networksneural-networksartificial-neural-networksfeedforward-neural-networkconvolutional-neural-networkstensorflow2
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

null 43 Dec 12, 2022
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.

Karttikeya Manglam 40 Nov 18, 2022
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

Miles Zhang 54 Dec 21, 2022
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.
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.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

Nikola Zubić 213 Dec 27, 2022
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.

JDAI-CV 2.8k Jan 7, 2023
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

Webis 42 Aug 14, 2022
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

null 1k Dec 28, 2022
🤗 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

Hugging Face 77.2k Jan 2, 2023
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-

gts3.org (SSLab@Gatech) 55 Oct 25, 2022
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
GitHub Repository
Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Yaoming Cai 5 Jul 18, 2022
Methods to get the probability of a changepoint in a time series.

Bayesian Changepoint Detection Methods to get the probability of a changepoint in a time series. Both online and offline methods are available. Read t

Johannes Kulick 554 Dec 30, 2022
A Tensorfflow implementation of Attend, Infer, Repeat

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

Adam Kosiorek 82 May 27, 2022
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
Scalable Multi-Agent Reinforcement Learning

Scalable Multi-Agent Reinforcement Learning 1. Featured algorithms: Value Function Factorization with Variable Agent Sub-Teams (VAST) [1] 2. Implement

3 Aug 02, 2022
SOTR: Segmenting Objects with Transformers [ICCV 2021]

SOTR: Segmenting Objects with Transformers [ICCV 2021] By Ruohao Guo, Dantong Niu, Liao Qu, Zhenbo Li Introduction This is the official implementation

186 Dec 20, 2022
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

Microsoft 247 Dec 25, 2022
This respository includes implementations on Manifoldron: Direct Space Partition via Manifold Discovery

Manifoldron: Direct Space Partition via Manifold Discovery This respository includes implementations on Manifoldron: Direct Space Partition via Manifo

dayang_wang 4 Apr 28, 2022
DCGAN LSGAN WGAN-GP DRAGAN PyTorch

Recommendation Our GAN based work for facial attribute editing - AttGAN. News 8 April 2019: We re-implement these GANs by Tensorflow 2! The old versio

Zhenliang He 408 Nov 30, 2022
Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies

SIGGRAPH 2021: Discovering Diverse Athletic Jumping Strategies project page paper demo video Prerequisites Important Notes We suspect there are bugs i

54 Dec 06, 2022
Unsupervised Image to Image Translation with Generative Adversarial Networks

Unsupervised Image to Image Translation with Generative Adversarial Networks Paper: Unsupervised Image to Image Translation with Generative Adversaria

Hao 71 Oct 30, 2022
An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

Deep-motion-editing This library provides fundamental and advanced functions to work with 3D character animation in deep learning with Pytorch. The co

1.2k Dec 29, 2022
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains

Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains This is an accompanying repository to the ICAIL 2021 pap

4 Dec 16, 2021
A pre-trained model with multi-exit transformer architecture.

ElasticBERT This repository contains finetuning code and checkpoints for ElasticBERT. Towards Efficient NLP: A Standard Evaluation and A Strong Baseli

fastNLP 48 Dec 14, 2022
Largest list of models for Core ML (for iOS 11+)

Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation We'v

Kedan Li 5.6k Jan 08, 2023
Computer Vision Script to recognize first person motion, developed as final project for the course "Machine Learning and Deep Learning"

Overview of The Code BaseColab/MLDL_FPAR.pdf: it contains the full explanation of our work Base Colab: it contains the base colab used to perform all

Simone Papicchio 4 Jul 16, 2022
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

Surag Nair 3.1k Jan 05, 2023
Graph Convolutional Networks for Temporal Action Localization (ICCV2019)

Graph Convolutional Networks for Temporal Action Localization This repo holds the codes and models for the PGCN framework presented on ICCV 2019 Graph

Runhao Zeng 318 Dec 06, 2022
Sudoku solver - A sudoku solver with python

sudoku_solver A sudoku solver What is Sudoku? Sudoku (Japanese: 数独, romanized: s

Sikai Lu 0 May 22, 2022
This repository contains a CBIR system that uses swin transformer to extract image's feature.

Swin-transformer based CBIR This repository contains a CBIR(content-based image retrieval) system. Here we use Swin-transformer to extract query image

JsHou 12 Nov 17, 2022