InceptionV4¶

InceptionV4: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Introduction¶

InceptionV4 studies whether the Inception module combined with Residual Connection can be improved. It is found that the structure of ResNet can greatly accelerate the training, and the performance is also improved. An Inception-ResNet v2 network is obtained, and a deeper and more optimized Inception v4 model is also designed, which can achieve comparable performance with Inception-ResNet v2.

Benchmark¶

				Pynative	Pynative	Graph	Graph
	Model	Top-1 (%)	Top-5 (%)	train (s/epoch)	Infer (ms)	train(s/epoch)	Infer (ms)	Download	Config
GPU	inception_v4			1702.063		1895.667		model	config
Ascend	inception_v4

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_v4 --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_v4 --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_v4 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/inception_v4-best.ckpt'