| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Auxiliary Classifier, 1x1 Convolution, Average Pooling, Batch Normalization, Convolution, Dropout, Dense Connections, Inception Module, ReLU, Max Pooling, Softmax |
| ID | googlenet |
| SHOW MORE |
GoogLeNet is a type of convolutional neural network based on the Inception architecture. It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block. An Inception network stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid.
To load a pretrained model:
import torchvision.models as models
googlenet = models.googlenet(pretrained=True)
Replace the model name with the variant you want to use, e.g. googlenet. 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.
@article{DBLP:journals/corr/SzegedyLJSRAEVR14,
author = {Christian Szegedy and
Wei Liu and
Yangqing Jia and
Pierre Sermanet and
Scott E. Reed and
Dragomir Anguelov and
Dumitru Erhan and
Vincent Vanhoucke and
Andrew Rabinovich},
title = {Going Deeper with Convolutions},
journal = {CoRR},
volume = {abs/1409.4842},
year = {2014},
url = {http://arxiv.org/abs/1409.4842},
archivePrefix = {arXiv},
eprint = {1409.4842},
timestamp = {Mon, 13 Aug 2018 16:48:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/SzegedyLJSRAEVR14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Image Classification
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | GoogleNet | Top 1 Accuracy | 69.78% | # 276 |
| Top 5 Accuracy | 89.53% | # 276 |
