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README.md | 1 year ago |
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.
Pynative | Pynative | Graph | Graph | ||||||
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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 |
The yaml config files that yield competitive results on ImageNet for different models are listed in the config
folder. To trigger training using preset yaml config.
comming soon
Here is the example for finetuning a pretrained InceptionV3 on CIFAR10 dataset using Adam 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.
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'
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