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Code and trained model for Computer Vision for Segmentation and Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Datase

Authors: Sagi Eppel;

Code and trained model for Computer Vision for Segmentation and Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Datase

Abstract

Code and trained models for the semantic segmentation FCN and Instance segmentation (GES net) neural nets used in: Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset The nets receive an image of material in vessel and segment and classify all the region in the image corresponding to vessels and various of material phases inside them https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004 Basically the net contained both the model and the trained weight and can be run as-is with no training for semantic segmentation net (PSP) and Generator evaluator selector Net (Ges net)

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Keywords

Computer vision, Fully Convolutional neural nets, Machine learing, Chemistry lab. Pytorch, Computer vision, Fully Convolutional neural nets, Machine learing, Chemistry lab. Pytorch

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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