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README.md | 1 year ago |
RepVGG, a vgg-style architecture that outperforms many complex models
Its main highlights are:
The model has a normal (aka feedforward) structure like vgg, without any other branches, each layer takes the output of its only previous layer as input, and feeds the output to its only next layer.
The body of the model uses only 3 × 3 conv and ReLU.
The specific architecture (including specific depth and layer width) is instantiated without automatic search, manual refinement, compound scaling, and other complicated designs.
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 | RepVGG_A0 | 71.98 | 90.36 | model | config | ||||
Ascend | RepVGG_A0 | 71.87 | 90.43 |
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 RepVGG_A0 on CIFAR10 dataset using Adam optimizer.
python train.py --model=RepVGG_A0 --pretrained --opt=adam --lr=0.001 ataset=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=RepVGG_A0 --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=RepVGG_A0 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/RepVGG_A0-best.ckpt'
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