Multiclass crater classification using fully convolutional residual dense network

dc.contributor.advisor	Khanna, Nitin
dc.contributor.author	Singh, Amrita
dc.date.accessioned	2022-08-31T10:59:37Z
dc.date.available	2022-08-31T10:59:37Z
dc.date.issued	2022
dc.identifier.citation	Singh, Amrita (2022). Multiclass crater classification using fully convolutional residual dense network. Gandhinagar: Indian Institute of Technology Gandhinagar, 93p. (Acc. No.: T00999).
dc.identifier.uri	https://repository.iitgn.ac.in/handle/123456789/8058
dc.description.statementofresponsibility	by Amrita Singh
dc.format.extent	ix, 93p.: hbk.; 30 cm.
dc.language.iso	en_US
dc.publisher	Indian Institute of Technology Gandhinagar
dc.subject	20250024
dc.subject	Crater Classification
dc.subject	Multiclass Crater Classification
dc.subject	Data Augmentation
dc.subject	Class Prediction
dc.subject	InceptionV3 Architecture
dc.title	Multiclass crater classification using fully convolutional residual dense network
dc.type	Thesis
dc.contributor.department	Electrical Engineering
dc.description.degree	M. Tech