This is a super simple ConvNet architecture that achieves over 80% top-1 accuracy on ImageNet with a stack of 3x3 conv and ReLU!
The training code and pretrained models will be updated upon the announcement of arxiv preprint (in two days).
Star me if you are interested.
We trained for 120 epochs with cosine learning rate decay from 0.1 to 0. We used 8 GPUs, global batch size of 256, weight decay of 1e-4 (no weight decay on fc.bias, bn.weight and bn.bias), and the same simple data preprocssing as the PyTorch official example:
trans = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_model = create_RepVGG_A0(deploy=False)
train train_model ...
deploy_model = repvgg_convert(train_model, create_RepVGG_A0, save_path='repvgg_deploy.pth')