|
gptkbp:instance_of
|
gptkb:television_channel
|
|
gptkbp:bfsLayer
|
6
|
|
gptkbp:bfsParent
|
gptkb:Res_Net-34
|
|
gptkbp:applies_to
|
Image Classification
|
|
gptkbp:architectural_style
|
gptkb:Deep_Learning
|
|
gptkbp:coat_of_arms
|
gptkb:18
|
|
gptkbp:developed_by
|
gptkb:Microsoft_Research
|
|
gptkbp:has
|
Convolutional Layers
Fully Connected Layers
Skip Connections
|
|
gptkbp:has_achievements
|
Top-5 accuracy of 92.3% on Image Net
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Res Net-18
|
|
gptkbp:is_compared_to
|
gptkb:Dense_Net
gptkb:Inception_Network
VGG Net
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:SVHN
gptkb:CIFAR-100
PASCALVOC
MSCOCO
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_known_for
|
Scalability
High Accuracy
Low Computational Cost
Robustness to Overfitting
Solving the Vanishing Gradient Problem
|
|
gptkbp:is_optimized_for
|
GPU Training
|
|
gptkbp:is_part_of
|
gptkb:Deep_Residual_Networks
gptkb:Artificial_Intelligence
gptkb:Image_Net_Challenge
gptkb:Res_Net_Family
Image Processing Techniques
Kaggle Competitions
|
|
gptkbp:is_popular_in
|
Academic Research
Industry Applications
|
|
gptkbp:is_used_for
|
Image Generation
Object Detection
Feature Extraction
Semantic Segmentation
|
|
gptkbp:is_used_in
|
gptkb:streaming_service
gptkb:Autonomous_Vehicles
Facial Recognition
Medical Imaging
Real-time Applications
Video Analysis
Computer Vision Tasks
|
|
gptkbp:performance
|
Deep Learning Models
|
|
gptkbp:predecessor
|
gptkb:Alex_Net
|
|
gptkbp:release_year
|
gptkb:2015
|
|
gptkbp:successor
|
gptkb:Res_Net-34
|
|
gptkbp:training
|
gptkb:Adam_Optimizer
gptkb:Image_Net_Dataset
Stochastic Gradient Descent
Learning Rate Scheduling
|
|
gptkbp:uses
|
gptkb:Batch_Normalization
Residual Learning
Re LU Activation Function
|