Title of Model: "Trained Model for the Semantic Segmentation of Corrosion Condition States"
Author(s): Eric Bianchi - Virginia Tech, Department of Civil and Environmental Engineering
Matt Hebdon - UT Austin, Department of Civil, Architectural & Environmental Engineering 
Corresponding Author Email Address: Eric Bianchi - beric7@vt.edu
Categories: GROUP 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING; GROUP 0905 CIVIL ENGINEERING.
Group: Department of Civil and Environmental Engineering - Structural Engineering and Materials 
Item Type: Software
Keywords: Semantic Segmentation; Machine Learning, Deep Learning; Structural Inspection; Corrosion; Bridge Inspection; Steel Corrosion; Model
Description: This contains four trained DeepLabV3+ models for the semantic segmentation of corrosion condition states. The models all were trained with a batch size of two, horizontal flip augmentations, and a resnet50 backbone. The models all were trained using image sizes of 512x512. The four models were trained with four different loss functions [cross entropy, L1-loss, L2-loss, and weighted cross-entropy]. The classes for this model are: [good (background), fair, poor, severe]. The classes are the condition states for corrosion. This model was trained using the corrosion dataset (DOI: 10.7294/16624663). After training, we were able to receive a F1 score of 86.67% for the l2 loss function model. More details of the training, the results, the dataset, and the code may be referenced in the journal article. The repository of the training and testing code may be accessed using the GitHub repository referenced in the journal article (If you are using the model in your work, please include both the journal article and the model citation).
Funding: National Science Foundation Grant No. IIS-1840044
Resource Title: "Development of Open-source Collaborative Structural Inspection Datasets"
Other References: For the codebase https://github.com/beric7/structural_inspection_main; For training data: https://doi.org/10.7294/16624663
License: CC-0
Publisher: University Libraries, Virginia Tech
Language: English
Location: Not applicable
Files/Folders in Dataset and Description of Files:
Corrosion Condition State Classification - Trained Model - Main Folder
	l1_loss - L1 loss function* (spectrum loss), augmented, batch of size of 2
		weights_27.pt - best model using validation set during training of 40 epochs

	l2_loss - L2 loss function* (spectrum loss), augmented, batch of size of 2
		weights_35.pt - best model using validation set during training of 40 epochs

	var_aug_batch_2_resnet50 - cross entropy loss function, augmented, batch of size of 2
		var_aug_batch_2_resnet50_weights_18.pt - best model using validation set 								during training of 40 epochs

	var_original_wwwbatch_2_plus - cross entropy loss function, augmented, weighted 						classes [0.1, 0.3,0.3,0.3] (background, fair, poor, 						severe), batch of size of 2
		var_original_wbatch_2_plus_weights_40.pt - best model using validation set 								during training of 40 epochs