mindcv/configs/googlenet

• GoogleNet
• • Introduction
  • Benchmark
  • Examples
  • • Train
    • Eval

GoogleNet

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GoogleNet: Going Deeper with Convolutions

Introduction

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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

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				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

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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'