GoogleNet

Introduction

GoogLeNet is a new deep learning structure proposed by Christian Szegedy in 2014. Prior to this, AlexNet, VGG and other structures achieved better training effects by increasing the depth (number of layers) of the network, but the increase in the number of layers It will bring many negative effects, such as overfit, gradient disappearance, gradient explosion, etc. The proposal of inception improves the training results from another perspective: it can use computing resources more efficiently, and can extract more features under the same amount of computing, thereby improving the training results.

Benchmark

Pynative Pynative Graph Graph
Model Top-1 (%) Top-5 (%) train (s/epoch) Infer (ms) train(s/epoch) Infer (ms) Download Config
GPU googlenet 260.898 260.434 model config
Ascend googlenet

Examples

Train

  • The yaml config files that yield competitive results on ImageNet for different models are listed in the configs folder. To trigger training using preset yaml config.

    comming soon

  • Here is the example for finetuning a pretrained GoogleNet on CIFAR10 dataset using Momentum optimizer.

    python train.py --model=googlenet --pretrained --opt=momentum --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download

Detailed adjustable parameters and their default value can be seen in config.py.

Eval

  • To validate the model, you can use validate.py. Here is an example to verify the accuracy of pretrained weights.

    python validate.py --model=googlenet --dataset=imagenet --val_split=val --pretrained

  • To validate the model, you can use validate.py. Here is an example to verify the accuracy of your training.

    python validate.py --model=googlenet --dataset=imagenet --val_split=val --ckpt_path='./ckpt/googlenet-best.ckpt'