How to train efficientnet on CIFAR-10 or CIFAR-100? Image size is 32x32.

The default model input size is 224~600. What adjustments should I make to fit CIFAR-10’s 32x32?

def efficientnet_params(model_name):
    """ Map EfficientNet model name to parameter coefficients. """
    params_dict = {
        # Coefficients:   width,depth,res,dropout
        'efficientnet-b0': (1.0, 1.0, 224, 0.2),
        'efficientnet-b1': (1.0, 1.1, 240, 0.2),
        'efficientnet-b2': (1.1, 1.2, 260, 0.3),
        'efficientnet-b3': (1.2, 1.4, 300, 0.3),
        'efficientnet-b4': (1.4, 1.8, 380, 0.4),
        'efficientnet-b5': (1.6, 2.2, 456, 0.4),
        'efficientnet-b6': (1.8, 2.6, 528, 0.5),
        'efficientnet-b7': (2.0, 3.1, 600, 0.5),
    }
    return params_dict[model_name]

https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/utils.py#L101-L114

[daixiangzi]

today i test cifar-10(raw image is 32*32) 1.use efficientnet-b3 input_size is 300,but acc is very low 2.use efficientnet-b3 input_size is modified 32,the result is normal on conclusion,need model input_size to fit raw image size??

[HoracceFeng]

Just an update: change image size to any nums seems OK since the maxpool and pad will do the work. I finally use 32*2=64 as input. Everything go smooth. I think this issue can be closed.