|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:bfsLayer
|
7
|
|
gptkbp:bfsParent
|
gptkb:Cresnet_2.0
|
|
gptkbp:contains
|
152 layers
|
|
gptkbp:developed_by
|
gptkb:Kaiming_He
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Cresnet 152
|
|
gptkbp:improves
|
gradient flow
|
|
gptkbp:industry
|
gptkb:musician
gptkb:Photographer
augmented reality
facial recognition
automated driving
|
|
gptkbp:is_adept_at
|
top-5 accuracy of 7.3% on Image Net
|
|
gptkbp:is_analyzed_in
|
transfer learning
feature extraction
fine-tuning
|
|
gptkbp:is_cited_in
|
research articles
academic papers
theses
|
|
gptkbp:is_compared_to
|
gptkb:Inception
gptkb:Dense_Net
VGG Net
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:SVHN
gptkb:CIFAR-100
top-5 accuracy
top-1 accuracy
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_known_for
|
flexibility
high performance
scalability
deep residual learning
|
|
gptkbp:is_optimized_for
|
gptkb:Adam_optimizer
Stochastic Gradient Descent
|
|
gptkbp:is_part_of
|
gptkb:Res_Net_family
Kaggle competitions
AI frameworks
AI challenges
deep learning libraries
computer vision toolkits
|
|
gptkbp:is_popular_in
|
computer vision
|
|
gptkbp:is_supported_by
|
NVIDIAGP Us
TP Us
|
|
gptkbp:is_used_for
|
image classification
|
|
gptkbp:is_used_in
|
object detection
image segmentation
|
|
gptkbp:performance
|
deep learning models
|
|
gptkbp:reduces
|
vanishing gradient problem
|
|
gptkbp:training
|
gptkb:Image_Net_dataset
data augmentation
dropout
batch normalization
|
|
gptkbp:utilizes
|
residual learning
|
|
gptkbp:year_created
|
gptkb:2015
|