Open glee1228 opened 5 years ago
parameter& Dataset :
Dataset : Sejong University building( in Seoul ) Image Dataset input image size : ( 128,128,3 ) netVLAD clustering num : 7 training triplet data size : 700 ( 100 * 3 pairs images per class ) optimizer : SGD(learning rate=0.01, decay=1e-6, momentum=0.9, nesterov=True) triplet loss margin : 1e-4
base model : VGG16( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : VGG16( pretrained weights : 'imagenet') total epochs : 25 train batch size : 64 T-SNE Result
base model : xception( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : inceptionV3( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : inceptionV3( pretrained weights : 'imagenet') total epochs : 20 train batch size : 64 T-SNE Result
base model : densenet121( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : densenet169( pretrained weights : 'imagenet') total epochs : 15 train batch size : 32 T-SNE Result
parameter& Dataset :
[고정 파라미터]
Dataset : Sejong University building( in Seoul ) Image Dataset input image size : ( 128,128,3 ) netVLAD clustering num : 7 training triplet data size : 700 ( 100 * 3 pairs images per class ) optimizer : SGD(learning rate=0.01, decay=1e-6, momentum=0.9, nesterov=True) triplet loss margin : 1e-4
[유동 파라미터]
base model : VGG16( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : VGG16( pretrained weights : 'imagenet') total epochs : 25 train batch size : 64 T-SNE Result
base model : xception( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : xception( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : inceptionV3( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : inceptionV3( pretrained weights : 'imagenet') total epochs : 20 train batch size : 64 T-SNE Result
base model : densenet121( pretrained weights : 'imagenet') total epochs : 15 train batch size : 64 T-SNE Result
base model : densenet169( pretrained weights : 'imagenet') total epochs : 15 train batch size : 32 T-SNE Result