Official code for paper "ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection"

Last update: Sep 21, 2022

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Overview

Official code for paper "ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection".

LRPDenseNet.py: code to create a DenseNet, based on original TorchVision model, but without in place ReLU and with an extra ReLU in transition layers.

ISNetFunctions.py: functions to define heatmap loss and relevance propagation.

ISNetLayers.py: functions to create an ISNet.

globals.py: global variables, for skip connections between classifier and LRP block.

TrainedModels: parameters for models trained in the paper.

Defining a DenseNet121 based ISNet: DenseNet=LRPDenseNet.densenet121(pretrained=False) #change last layer if needed net=ISNetLayers.IsDense(DenseNet,heat=True,e=1e-2,device='cuda:0', Zb=True)

Owner

Pedro Ricardo Ariel Salvador Bassi

Eletrical engineer and master's student at UNICAMP, University of Campinas. Main interests: machine learning, neural networks and python.

GitHub Repository

This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

The-Emergence-of-Objectness This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

44 Oct 08, 2022

Official code for paper "ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection"

Related tags

Overview

Owner

Pedro Ricardo Ariel Salvador Bassi

This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

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