| Training Techniques | Weight Decay, SGD with Momentum |
|---|---|
| Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
| ID | alexnet |
| SHOW MORE |
AlexNet is a classic convolutional neural network architecture. It consists of convolutions, max pooling and dense layers as the basic building blocks
To load a pretrained model:
import torchvision.models as models
squeezenet = models.alexnet(pretrained=True)
Replace the model name with the variant you want to use, e.g. alexnet. 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/Krizhevsky14,
author = {Alex Krizhevsky},
title = {One weird trick for parallelizing convolutional neural networks},
journal = {CoRR},
volume = {abs/1404.5997},
year = {2014},
url = {http://arxiv.org/abs/1404.5997},
archivePrefix = {arXiv},
eprint = {1404.5997},
timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
biburl = {https://dblp.org/rec/journals/corr/Krizhevsky14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Image Classification
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | AlexNet | Top 1 Accuracy | 56.55% | # 306 |
| Top 5 Accuracy | 79.09% | # 306 |
