| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg11 |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Batch Normalization, Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg11_bn |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg13 |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Batch Normalization, Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg13_bn |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg16 |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Batch Normalization, Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg16_bn |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg19 |
| SHOW MORE |
| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Batch Normalization, Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | vgg19_bn |
| SHOW MORE |
VGG is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.
To load a pretrained model:
import torchvision.models as models
vgg16 = models.vgg16(pretrained=True)
Replace the model name with the variant you want to use, e.g. vgg16. You can find
the IDs in the model summaries at the top of this page.
To evaluate the model, use the image classification recipes from the library.
python train.py --test-only --model='<model_name>'
You can follow the torchvision recipe on GitHub for training a new model afresh.
@InProceedings{Simonyan15,
author = "Karen Simonyan and Andrew Zisserman",
title = "Very Deep Convolutional Networks for Large-Scale Image Recognition",
booktitle = "International Conference on Learning Representations",
year = "2015",
}
Image Classification on ImageNet
| MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
|---|---|---|
| VGG-19 with batch normalization | 74.24% | 91.85% |
| VGG-16 with batch normalization | 73.37% | 91.5% |
| VGG-19 | 72.38% | 90.88% |
| VGG-16 | 71.59% | 90.38% |
| VGG-13 with batch normalization | 71.55% | 90.37% |
| VGG-11 with batch normalization | 70.38% | 89.81% |
| VGG-13 | 69.93% | 89.25% |
| VGG-11 | 69.02% | 88.63% |
