GoogleNet-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Going Deeper with Convolutions.

Table of contents

• GoogleNet-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • How Test and Train
    • Test
    • Train model
    • Resume train model
  • Result
  • Contributing
  • Credit
    • Going Deeper with Convolutions

Download weights

• Google Driver
• Baidu Driver

Download datasets

Contains MNIST, CIFAR10&CIFAR100, TinyImageNet_200, MiniImageNet_1K, ImageNet_1K, Caltech101&Caltech256 and more etc.

• Google Driver
• Baidu Driver

Please refer to README.md in the data directory for the method of making a dataset.

How Test and Train

Both training and testing only need to modify the config.py file.

Test

• line 29: model_arch_name change to googlenet.
• line 31: model_num_classes change to 1000.
• line 33: mode change to test.
• line 88: model_weights_path change to ./results/pretrained_models/GOOGLENET-ImageNet_1K-64f6524f.pth.tar.

python3 test.py

Train model

• line 29: model_arch_name change to googlenet.
• line 31: model_num_classes change to 1000.
• line 33: mode change to train.
• line 47: pretrained_model_weights_path change to ./results/pretrained_models/GoogleNet-ImageNet_1K-32d70693.pth.tar.

python3 train.py

Resume train model

• line 29: model_arch_name change to googlenet.
• line 31: model_num_classes change to 1000.
• line 33: mode change to train.
• line 50: resume change to ./samples/GOOGLENET-ImageNet_1K/epoch_xxx.pth.tar.

python3 train.py

Result

Source of original paper results: https://arxiv.org/pdf/1409.4842v1.pdf)

In the following table, the top-x error value in () indicates the result of the project, and - indicates no test.

# Download `GoogleNet-ImageNet_1K-32d70693.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py 

Input:

Output:

Build `googlenet` model successfully.
Load `googlenet` model weights `/GoogleNet-PyTorch/results/pretrained_models/GoogleNet-ImageNet_1K-32d70693.pth.tar` successfully.
tench, Tinca tinca                                                          (90.46%)
armadillo                                                                   (2.23%)
barracouta, snoek                                                           (0.70%)
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus (0.26%)
mud turtle                                                                  (0.17%)

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

Going Deeper with Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

Abstract

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 ( ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multiscale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

[Paper]

@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}