A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
EVALUATING CONVNET AND TRANSFORMER BASED SELF-SUPERVISED ALGORITHMS FOR BUILDING ROOF FORM CLASSIFICATION
This research paper presents a comprehensive evaluation of various self-supervised learning models for building roof type classification. We conduct linear evaluation experiments for the models pretrained on both the ImageNet1K dataset and a custom building roof type dataset to assess the models’ performance for the roof type classification task. The results demonstrate the effectiveness of the ViT-based BEiTV2 model, which outperforms other models on both datasets, achieving an accuracy of 96.8% from the model pretrained on ImageNet1K dataset and 92.67% on the model pretrained on building roof type dataset. The class activation maps further validate the strong performance of MoCoV3, BarlowTwins, and DenseCL models. These findings emphasize the potential of self-supervised learning for accurate building roof type classification, with the ViT-based BEiTV2 model showcasing state-of-the-art results.
EVALUATING CONVNET AND TRANSFORMER BASED SELF-SUPERVISED ALGORITHMS FOR BUILDING ROOF FORM CLASSIFICATION
This research paper presents a comprehensive evaluation of various self-supervised learning models for building roof type classification. We conduct linear evaluation experiments for the models pretrained on both the ImageNet1K dataset and a custom building roof type dataset to assess the models’ performance for the roof type classification task. The results demonstrate the effectiveness of the ViT-based BEiTV2 model, which outperforms other models on both datasets, achieving an accuracy of 96.8% from the model pretrained on ImageNet1K dataset and 92.67% on the model pretrained on building roof type dataset. The class activation maps further validate the strong performance of MoCoV3, BarlowTwins, and DenseCL models. These findings emphasize the potential of self-supervised learning for accurate building roof type classification, with the ViT-based BEiTV2 model showcasing state-of-the-art results.
EVALUATING CONVNET AND TRANSFORMER BASED SELF-SUPERVISED ALGORITHMS FOR BUILDING ROOF FORM CLASSIFICATION
G. Mutreja (author) / K. Bittner (author)
2023
Article (Journal)
Electronic Resource
Unknown
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