InceptionV3

Introduction

InceptionV3 is an upgraded version of GoogleNet. One of the most important improvements of V3 is Factorization, which decomposes 7x7 into two one-dimensional convolutions (1x7, 7x1), and 3x3 is the same (1x3, 3x1), such benefits, both It can accelerate the calculation (excess computing power can be used to deepen the network), and can split 1 conv into 2 convs, which further increases the network depth and increases the nonlinearity of the network. It is also worth noting that the network input from 224x224 has become 299x299, and 35x35/17x17/8x8 modules are designed more precisely. In addition, V3 also adds batch normalization, which makes the model converge more quickly, which plays a role in partial regularization and effectively reduces overfitting.

Benchmark

Pynative Pynative Graph Graph
Model Top-1 (%) Top-5 (%) train (s/epoch) Infer (ms) train(s/epoch) Infer (ms) Download Config
GPU inception_v3 1145.248 1063.01 model config
Ascend inception_v3

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 InceptionV3 on CIFAR10 dataset using Momentum optimizer.

    python train.py --model=inception_v3 --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=inception_v3 --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=inception_v3 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/inception_v3-best.ckpt'