Hi everyone, I am thinking how to model the following problem using fastai.
I want to train a classifier that given a subset of images is able to pick the image with the highest resolution relative to the other images in the subset.
My images are stored in a data-frame, one for each row, and each row specifies an Id to group the images in a specific group.
So I have groups with n images in it where n can vary, I want to be able to handle groups with different sizes.
The labels are binary, 1 for the image with the highest resolution in the group and 0 for all the other.
There is also a column that indicates me how to split my data in train and validation sets.
Id - ImageName - Label - is_valid
1, image_1.png, 1, False
1, image_2.png, 0, False
1, image_3.png, 0, False
1, image_4.png, 0, False
2, image_5.png, 1, False
2, image_6.png, 0, False
3, image_7.png, 0, True
3, image_8.png, 0, True
3, image_9.png, 1, True
3, image_10.png, 0, True
3, image_11.png, 0, True
3, image_12.png, 0, True
How would you model this with the DataBlock API?
Thanks in advance !!!