|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:bfsLayer
|
6
|
|
gptkbp:bfsParent
|
gptkb:Crestron
|
|
gptkbp:applies_to
|
Image Classification
|
|
gptkbp:architectural_style
|
gptkb:Deep_Residual_Networks
|
|
gptkbp:developed_by
|
gptkb:Kaiming_He
|
|
gptkbp:has_achievements
|
State-of-the-art Performance
|
|
gptkbp:has_variants
|
gptkb:Cresnet_101
gptkb:Cresnet_152
gptkb:Cresnet_50
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Cresnet 2.0
|
|
gptkbp:improves
|
Gradient Flow
|
|
gptkbp:influenced_by
|
VGG Net
|
|
gptkbp:is_compared_to
|
gptkb:Mobile_Net
gptkb:Dense_Net
gptkb:Res_Net
gptkb:Inception_Networks
gptkb:Squeeze_Net
|
|
gptkbp:is_evaluated_by
|
gptkb:Image_Net
gptkb:CIFAR-10
gptkb:CIFAR-100
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_known_for
|
gptkb:Batch_Normalization
Flexibility
Scalability
Robustness
High Accuracy
Reduced Overfitting
Skip Connections
Layer Normalization
Efficient Training
Deep Feature Learning
|
|
gptkbp:is_optimized_for
|
Deep Learning Tasks
|
|
gptkbp:is_part_of
|
gptkb:AI_Research
gptkb:Artificial_Intelligence
gptkb:software_framework
gptkb:Deep_Learning
Image Recognition
Object Detection
Feature Extraction
Deep Learning Frameworks
Semantic Segmentation
Neural Network Models
Computer Vision Research
|
|
gptkbp:is_used_by
|
gptkb:physicist
Industry Professionals
|
|
gptkbp:is_used_in
|
gptkb:Autonomous_Vehicles
gptkb:viewpoint
gptkb:software
gptkb:software_framework
gptkb:studio
Time Series Analysis
Facial Recognition
Medical Imaging
Video Analysis
|
|
gptkbp:release_year
|
gptkb:2016
|
|
gptkbp:supports
|
gptkb:streaming_service
|
|
gptkbp:training
|
Large Datasets
|
|
gptkbp:uses
|
Residual Learning
|