I’m new about cv, but I have some background.

I feel weird that in Lesson 1 we use a resnet to classify mnist data “3” and “7”.

In my memory, the input size of resnet is 3*224*224, but for this mnist data, the size is exactly 3*28*28.

Surely, we can use print(models.resnet()) to see its structure, but I cannot find how to know its input tensor size .

The print result is as follows:

Test: None, model=Sequential(

(0): Sequential(

(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)

(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)…

So can anyone let me know how could this model fit the mnist data’s size and how to know the input image’s size of the model(I supposed that the default size is 224*224)?