Statements (33)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:deep_convolutional_neural_network
|
| gptkbp:activatedBy |
gptkb:ReLU
|
| gptkbp:architecture |
residual network
|
| gptkbp:author |
gptkb:Shaoqing_Ren
gptkb:Xiangyu_Zhang gptkb:Jian_Sun gptkb:Kaiming_He |
| gptkbp:category |
gptkb:computer_vision_model
|
| gptkbp:developedBy |
gptkb:Microsoft_Research
|
| gptkbp:digestSize |
224x224 pixels
|
| gptkbp:hasParameterCount |
60 million
|
| gptkbp:hasVariant |
gptkb:ResNet-101
gptkb:ResNet-18 gptkb:ResNet-34 gptkb:ResNet-50 |
| gptkbp:introduced |
gptkb:Deep_Residual_Learning_for_Image_Recognition
|
| gptkbp:introducedIn |
2015
|
| gptkbp:level |
152
|
| gptkbp:maximumDepth |
152 layers
|
| gptkbp:platform |
gptkb:TensorFlow
gptkb:Keras gptkb:Caffe gptkb:PyTorch |
| gptkbp:trainer |
gptkb:ImageNet
|
| gptkbp:usedFor |
feature extraction
image classification object detection |
| gptkbp:uses |
batch normalization
skip connections |
| gptkbp:won |
ILSVRC 2015 classification task
|
| gptkbp:bfsParent |
gptkb:Residual_Network
|
| gptkbp:bfsLayer |
8
|
| https://www.w3.org/2000/01/rdf-schema#label |
ResNet-152
|