|
gptkbp:instanceOf
|
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
|
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
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
ResNet-152
|
|
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
gptkb:EfficientNet-B1
gptkb:EfficientNet-B3
gptkb:EfficientNet-B5
gptkb:EfficientNet-B6
gptkb:EfficientNet-B7
|
|
gptkbp:bfsLayer
|
7
|