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