First the pre-trained Resnet has been trained on ImageNet database 69 which has 1000 categories 251.
So the output is a vector containing 1000 float or double scalar values. Each value represents the score of the image belonging to the category matching its index. Therefore you don’t need to apply the softmax function on the output as you’re not training the model, you’re using it to make predictions. You just look for the index that holds the max score and this one corresponds to the category predicted by the model.

Example:
Suppose we have 3 categories instead of 1000 and extension is straightforward.
Cateogories are : cat=0, dog=1, puma=2. Here the output is a 3 scalar float values vector corresponding to the score of each category. So an output of  outp = [2.3, 7.22, -4.25]
means score(Image is cat) = 2.3,  score(Image is dog) = 7.22 and score(Image is puma) = -4.25.
Index of the max score is 1 in outp. So the model predicts that the image is a dog.

The remaining thing is what are the category names of each one of 0, …, 999 indexes of the ImageNet. You can find them here ImageNet index - class name 449
Hopefully it’s clear.