ok, @AjayTalati, you can try license number plate dataset.
there are six or seven digits/letters in plate, if choose 7,
every character has 36 possible classes(A-Z,0-9)
so every number plate has  736 labels as targets, the value 1 indicate the position related to a special character’s value,i36+k(0<=i<=num_character, 0<=k<=35), i indicate the position, and k indicate the value of character.
for example,if target[49]=1, means 1*36+13, the 2nd charater is  ‘M’
i’m also learning pytorch, and take it as an exercise,
the input is BCHW, using multilabelmarginloss()