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The core idea of SKNet: SK Convolution
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 | sknet50 | model | config | ||||||
Ascend | sknet50 |
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 SKNet on CIFAR10 dataset using Adam optimizer.
python train.py --model=sknet50 --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=sknet50 --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=sknet50 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/sknet50-best.ckpt'
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