Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:neural_networks
|
| gptkbp:achieved_top5_accuracy |
89.8% on Image Net
|
| gptkbp:architecture |
gptkb:Deep_Learning
|
| gptkbp:coat_of_arms |
Convolutional layers
Fully connected layers Pooling layers |
| gptkbp:developed_by |
gptkb:Visual_Geometry_Group
|
| gptkbp:first_introduced |
gptkb:2014
|
| gptkbp:has_function |
138 million
|
| https://www.w3.org/2000/01/rdf-schema#label |
VGG family
|
| gptkbp:includes |
gptkb:VGG16
gptkb:VGG19 |
| gptkbp:influenced |
gptkb:Res_Net
|
| gptkbp:influenced_by |
gptkb:Le_Net
|
| gptkbp:is_based_on |
Alex Net architecture
|
| gptkbp:is_evaluated_by |
gptkb:CIFAR-10
gptkb:CIFAR-100 |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Py_Torch |
| gptkbp:is_known_for |
High accuracy
Simplicity and depth Layer-wise training Large model size |
| gptkbp:is_part_of |
Deep Learning frameworks
Neural network architectures Computer Vision models |
| gptkbp:is_popular_in |
gptkb:Computer_Vision
Academic research Industry applications |
| gptkbp:is_related_to |
gptkb:cloud_computing
gptkb:Dropout Backpropagation Performance metrics Embedded systems Data augmentation Real-time applications Transfer learning Hyperparameter tuning Model evaluation Fine-tuning Optimization algorithms Regularization techniques Model deployment Gradient descent Batch normalization Training datasets Activation functions Loss functions Image preprocessing Convolutional layers Feature maps Pooling operations |
| gptkbp:is_trained_in |
gptkb:Image_Net_dataset
|
| gptkbp:is_used_for |
Image segmentation
Feature extraction Object detection Face recognition Style transfer |
| gptkbp:is_used_in |
gptkb:stage_adaptation
|
| gptkbp:notable_for |
Image classification
|
| gptkbp:performance |
gptkb:Image_Net_competition
71.3% on Image Net |
| gptkbp:uses |
Re LU activation function
|
| gptkbp:bfsParent |
gptkb:VGG16
|
| gptkbp:bfsLayer |
6
|