gptkbp:instance_of
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gptkb:neural_networks
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gptkbp:architecture
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gptkb:Deep_Learning
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gptkbp:based_on
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Skip Connections
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gptkbp:coat_of_arms
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gptkb:swimming_pool
Activation Function
Convolutional Layer
Fully Connected Layer
Batch Normalization Layer
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gptkbp:developed_by
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gptkb:Microsoft_Research
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gptkbp:has_applications_in
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gptkb:Augmented_Reality
gptkb:Autonomous_Vehicles
gptkb:robotics
Medical Imaging
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gptkbp:has_variants
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gptkb:Res_Net-110
gptkb:Res_Net-164
gptkb:Res_Net-101
gptkb:Res_Net-152
gptkb:Res_Net-50
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https://www.w3.org/2000/01/rdf-schema#label
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Res Net Family
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gptkbp:improves
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Gradient Flow
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gptkbp:introduced_in
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gptkb:2015
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gptkbp:is_applied_in
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Object Detection
Face Recognition
Semantic Segmentation
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gptkbp:is_influenced_by
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gptkb:VGG_Network
gptkb:Alex_Net
gptkb:Google_Net
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gptkbp:is_known_for
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Efficiency
Flexibility
Scalability
Robustness
High Accuracy
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gptkbp:is_popular_in
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Computer Vision Community
Deep Learning Research
AI Industry
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gptkbp:is_used_in
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gptkb:COCO_Challenge
gptkb:Image_Net_Challenge
Kaggle Competitions
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gptkbp:performance
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Inference Time
Top-1 Accuracy
Top-5 Accuracy
FLOPs
Parameters Count
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gptkbp:special_features
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Residual Learning
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gptkbp:supports
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gptkb:stage_adaptation
Fine-tuning
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gptkbp:used_in
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Image Classification
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gptkbp:won
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gptkb:ILSVRC_2015
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gptkbp:bfsParent
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gptkb:Res_Net50
gptkb:Res_Net-101
gptkb:Res_Net-152
gptkb:Res_Net-50
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gptkbp:bfsLayer
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6
|