Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)

Overview

AlbUNet-1D-2D-Tensorflow-Keras

This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed in Tensorflow-Keras. The code supports Deep Supervision, AutoEncoder mode, Guided Attention and other options. The segmentation models can be used for binary or multiclass segmentation, or for regression tasks.

Models supported [1]

  1. AlbUNet18
  2. AlbUNet34
  3. AlbUNet50
  4. AlbUNet101
  5. AlbUNet152

AlbUNet

AlbUNet has a ResNet based Encoder and traditional UNet based Decoder, as shown in the Figure below for ALbUNet34, which uses ResNet34 as the Encoder.
AlbUNet Architecture
AlbUNet Architecture

Supported Features

The speciality about this model is its flexibility, such as:

  1. The user can choose any of the 5 available AlbUNet variants for either 1D or 2D Segmentation tasks.
  2. The models can be used for Binary or Multi-Class Classification, or Regression type Segmentation tasks.
  3. The models allow Deep Supervision [2] with flexibility during Segmentation.
  4. The segmentation models can also be used as Autoencoders [3] for Feature Extraction.
  5. The Segmentation Models can be Attention Guided [4].
  6. Number of input kernel/filter, commonly known as the Width of the model can be varied.
  7. Number of classes for Classification tasks and number of extracted features for Regression tasks can be varied.
  8. Number of Channels in the Input Dataset can be varied.

Mentionable that the 2D version of AlbUNet can also be used in Transfer Learning from previously trained weights (e.g., ImageNet), just the encoder blocks should be replaced with the trained model layers.

References

[1] A. Shvets, V. Iglovikov, A. Rakhlin, and A. A. Kalinin, “Angiodysplasia detection and localization using deep convolutional neural networks,” arXiv.org, 21-Apr-2018. [Online]. Available: https://arxiv.org/abs/1804.08024. [2] Zhou, Z., Siddiquee, M., Tajbakhsh, N., & Liang, J. (2021). UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. Arxiv-vanity.com. Retrieved 30 August 2021, from https://www.arxiv-vanity.com/papers/1912.05074/.
[3] Zhou, Z., Siddiquee, M., Tajbakhsh, N., & Liang, J. (2021). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. arXiv.org. Retrieved 30 August 2021, from https://arxiv.org/abs/1807.10165.
[4] M. Noori, A. Bahri, and K. Mohammadi, “Attention-guided version of 2D UNET for automatic brain tumor segmentation,” arXiv.org, 04-Apr-2020. [Online]. Available: https://arxiv.org/abs/2004.02009.

Owner
Sakib Mahmud
Research Assistant | Electrical Engineer | Machine Learning Engineer
Sakib Mahmud
The source code of CVPR17 'Generative Face Completion'.

GenerativeFaceCompletion Matcaffe implementation of our CVPR17 paper on face completion. In each panel from left to right: original face, masked input

Yijun Li 313 Oct 18, 2022
Repository for the semantic WMI loss

Installation: pip install -e . Installing DL2: First clone DL2 in a separate directory and install it using the following commands: git clone https:/

Nick Hoernle 4 Sep 15, 2022
A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

A Pytorch Implementation of Source data‐free domain adaptation of object detector through domain‐specific perturbation Please follow Faster R-CNN and

1 Dec 25, 2021
Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution

Single Image Super-Resolution with EDSR, WDSR and SRGAN A Tensorflow 2.x based implementation of Enhanced Deep Residual Networks for Single Image Supe

Martin Krasser 1.3k Jan 06, 2023
[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

[NeurIPS 2021] Galerkin Transformer: linear attention without softmax Summary A non-numerical analyst oriented explanation on Toward Data Science abou

Shuhao Cao 159 Dec 20, 2022
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

HUAWEI Noah's Ark Lab 3k Jan 08, 2023
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

Meta Research 283 Dec 30, 2022
Unsupervised Feature Ranking via Attribute Networks.

FRANe Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with nodes that correspond to the features

7 Sep 29, 2022
Awesome Transformers in Medical Imaging

This repo supplements our Survey on Transformers in Medical Imaging Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat,

Fahad Shamshad 666 Jan 06, 2023
Resco: A simple python package that report the effect of deep residual learning

resco Description resco is a simple python package that report the effect of dee

Pierre-Arthur Claudé 1 Jun 28, 2022
Continual Learning of Electronic Health Records (EHR).

Continual Learning of Longitudinal Health Records Repo for reproducing the experiments in Continual Learning of Longitudinal Health Records (2021). Re

Jacob 7 Oct 21, 2022
Ludwig Benchmarking Toolkit

Ludwig Benchmarking Toolkit The Ludwig Benchmarking Toolkit is a personalized benchmarking toolkit for running end-to-end benchmark studies across an

HazyResearch 17 Nov 18, 2022
Code for our ALiBi method for transformer language models.

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation This repository contains the code and models for our paper Tra

Ofir Press 211 Dec 31, 2022
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

Vladislav Kurenkov 4 Dec 14, 2021
Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

Box_Discretization_Network This repository is built on the pytorch [maskrcnn_benchmark]. The method is the foundation of our ReCTs-competition method

Yuliang Liu 266 Nov 24, 2022
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
A new test set for ImageNet

ImageNetV2 The ImageNetV2 dataset contains new test data for the ImageNet benchmark. This repository provides associated code for assembling and worki

186 Dec 18, 2022
PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

0 Mar 01, 2022
[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

InvCompress Official Pytorch Implementation for "Enhanced Invertible Encoding for Learned Image Compression", ACMMM 2021 (Oral) Figure: Our framework

96 Nov 30, 2022
Reviving Iterative Training with Mask Guidance for Interactive Segmentation

This repository provides the source code for training and testing state-of-the-art click-based interactive segmentation models with the official PyTorch implementation

Visual Understanding Lab @ Samsung AI Center Moscow 406 Jan 01, 2023