Inception v4
E472889
Inception architecture variant
convolutional neural network architecture
deep learning model
image classification model
Inception v4 is an advanced deep convolutional neural network model for image recognition that refines and extends earlier Inception architectures to achieve higher accuracy and efficiency.
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
Inception architecture variant
ⓘ
convolutional neural network architecture ⓘ deep learning model ⓘ image classification model ⓘ |
| achieves | state-of-the-art accuracy on ImageNet at time of publication ⓘ |
| architectureType | deep convolutional neural network ⓘ |
| basedOn | Inception architecture NERFINISHED ⓘ |
| benchmark | ImageNet NERFINISHED ⓘ |
| category | feedforward neural network ⓘ |
| contains |
multiple Inception-A blocks
ⓘ
multiple Inception-B blocks ⓘ multiple Inception-C blocks ⓘ reduction blocks between Inception stages ⓘ |
| describedIn | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning NERFINISHED ⓘ |
| designGoal |
computational efficiency
ⓘ
higher accuracy ⓘ |
| developedBy |
Google
NERFINISHED
ⓘ
Google Brain NERFINISHED ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ |
| follows | Inception v3 NERFINISHED ⓘ |
| implementationAvailableIn |
Keras
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ⓘ
TensorFlow NERFINISHED ⓘ |
| improvesUpon |
GoogLeNet
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ⓘ
Inception v2 NERFINISHED ⓘ Inception v3 NERFINISHED ⓘ |
| inputDomain | natural images ⓘ |
| inputType | RGB images ⓘ |
| license | open source implementation available ⓘ |
| networkDepth | very deep ⓘ |
| optimization | batch normalization ⓘ |
| paperAuthorsInclude |
Alex Alemi
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ⓘ
Christian Szegedy NERFINISHED ⓘ Sergey Ioffe NERFINISHED ⓘ Vincent Vanhoucke NERFINISHED ⓘ |
| relatedTo |
Inception-ResNet-v1
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ⓘ
Inception-ResNet-v2 NERFINISHED ⓘ |
| task |
image classification
ⓘ
image recognition ⓘ |
| trainingDataset | ImageNet Large Scale Visual Recognition Challenge dataset NERFINISHED ⓘ |
| typicalUseCase |
feature extraction from images
ⓘ
transfer learning for vision tasks ⓘ |
| uses |
Inception modules
ⓘ
factorized convolutions ⓘ grid size reduction blocks ⓘ |
| yearProposed | 2016 ⓘ |
Referenced by (1)
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