Python Deep-convolutional Resources

diffusion-channels Source code for paper "Deep Diffusion Models for Robust Channel Estimation". Generic flow: Use 'matlab/main.mat' to generate traini

Interpreting Language Models Through Knowledge Graph Extraction Idea: How do we interpret what a language model learns at various stages of training?

time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

ROCKET + MINIROCKET ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge D

PySurvival What is Pysurvival ? PySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or p

Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under

Telemanom (v2.0) v2.0 updates: Vectorized operations via numpy Object-oriented restructure, improved organization Merge branches into single branch fo

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil

NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture

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

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi

Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co

Metaflow Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

GSCNN This is the official code for: Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler

PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to

DFANet This repo is an unofficial pytorch implementation of DFANet:Deep Feature Aggregation for Real-Time Semantic Segmentation log 2019.4.16 After 48

Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

UPSNet: A Unified Panoptic Segmentation Network Introduction UPSNet is initially described in a CVPR 2019 oral paper. Disclaimer This repository is te

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation This repository contains the source code of our paper, ESPNet (acc

Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor

TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under

Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s

Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel

Segmentation-Aware Convolutional Networks Using Local Attention Masks [Project Page] [Paper] Segmentation-aware convolution filters are invariant to b

MultiNet MultiNet is able to jointly perform road segmentation, car detection and street classification. The model achieves real-time speed and state-

PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+

Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation: Work In Progress, Results can't be replicated yet with the m

FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th

Introduction This repo contains the code to train and evaluate FC-DenseNets as described in The One Hundred Layers Tiramisu: Fully Convolutional Dense

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

ENet This work has been published in arXiv: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. Packages: train contains too

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git

Keras-FCN This repository contains my implementation of Fully Convolutional Networks in Keras (Tensorflow backend). Currently, semantic segmentation c

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the

FCN-semantic-segmentation Simple end-to-end semantic segmentation using fully convolutional networks [1]. Takes a pretrained 34-layer ResNet [2], remo

Pytorch for Semantic Segmentation This repo has been deprecated currently and I will not maintain it. Meanwhile, I strongly recommend you can refer to

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w

FCN.tensorflow Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (FCNs). The implementation is largely based on the

FCN_via_Keras FCN FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network

MatConvNet implementation of the FCN models for semantic segmentation This package contains an implementation of the FCN models (training and evaluati

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute

SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Caffe SegNet This is a modified version of Caffe which supports the SegNet architecture As described in SegNet: A Deep Convolutional Encoder-Decoder A

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

Retina blood vessel segmentation with a convolution neural network (U-net) This repository contains the implementation of a convolutional neural netwo

Real-Time Semantic Segmentation in Mobile device This project is an example project of semantic segmentation for mobile real-time app. The architectur

Tensorflow Unet Warning This project is discontinued in favour of a Tensorflow 2 compatible reimplementation of this project found under https://githu

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

ZF_UNET_224 Pretrained Model Modification of convolutional neural net "UNET" for image segmentation in Keras framework Requirements Python 3.*, Keras

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

A combination of autoregressors and autoencoders using XLNet for sentiment analysis Abstract In this paper sentiment analysis has been performed in or

AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed

Transformers: from NLP to CV This is a practical introduction to Transformers from Natural Language Processing (NLP) to Computer Vision (CV) Introduct

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

project discription An implementation of the Keyword-BERT model mentioned in my paper Keyword-Attentive Deep Semantic Matching (Plz cite this github r

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num

딥소트 수정중 Yolov5 + Deep Sort with PyTorch Introduction This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of obj

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

FPS-Net Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation", accepted by ISPRS journal of Photogrammetry

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C

d3rlpy: An offline deep reinforcement learning library d3rlpy is an offline deep reinforcement learning library for practitioners and researchers. imp

Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech This repository is the official implementation of "Meta-TTS: Meta-Learning for Few

Theme Transformer This is the official implementation of Theme Transformer. Checkout our demo and paper : Demo | arXiv Environment: using python versi

Laser device for neutralizing - mosquitoes, weeds and pests (in progress) Here I will post information for creating a laser device. A warning!! How It

EquivariantSelfAttention An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astro

Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu

Multiscale Deep Equilibrium Models 💥 💥 💥 💥 This repo is deprecated and we will soon stop actively maintaining it, as a more up-to-date (and simple

Image Inpainting What is Image Inpainting Image inpainting is a restorative process that allows for the fixing or removal of unwanted parts within ima

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

Graduation-Project Classification of EEG data using Deep Learning Epilepsy is the most common neurological disease in the world. Epilepsy occurs as a

Hourglass Transformer - Pytorch (wip) Implementation of Hourglass Transformer, in Pytorch. It will also contain some of my own ideas about how to make

Differentiable SVD Introduction This repository contains: The official Pytorch implementation of ICCV21 paper Why Approximate Matrix Square Root Outpe

Pri3D: Can 3D Priors Help 2D Representation Learning? [ICCV 2021] Pri3D leverages 3D priors for downstream 2D image understanding tasks: during pre-tr

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Human Trajectory Prediction via Counterfactual Analysis (CausalHTP) The official PyTorch code implementation of "Human Trajectory Prediction via Count

UEPNet (ICCV2021 Poster Presentation) This repository contains codes for the official implementation in PyTorch of UEPNet as described in Uniformity i

InterpretableMDE A PyTorch implementation for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper. arXiv link: https://arxiv.or

DIP-denosing This is a code repo for Rethinking Deep Image Prior for Denoising (ICCV 2021). Addressing the relationship between Deep image prior and e

Sketch Your Own GAN Project | Paper | Youtube | Slides Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to mat

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition How Fast Compare to Other Zero-Shot NAS Proxies on CIFAR-10/100 Pre-trained Model