Scenario: I am trying to create a dataset with images of choice for different animal classes. I am going to train those images for classification using CNN.
Problem: Let's assume I somehow don't have the privilege to collect too many images and was only able to collect a few of them for each class. Here's the list:
| id | animal | # | |----|--------------|-------| | 1 | Baboon | 800 | | 2 | Fox | 1000 | | 3 | Hyena | 5000 | | 4 | Giraffe | 43 | | 5 | Zebra | 88 | | 6 | Hippopotamus | 233 | | 7 | Yak | 578 | | 8 | Polar Bear | 456 | | 9 | Lion | 3442 | | 10 | Indian Tiger | 40000 |
I have three questions.
Is this a good dataset to train the CNN model? I am worried about the quantity each class has.
Will it be helpful if I augment the data? I think I am going to augment it.
In the future, the above-mentioned dataset is going to increase. So there is a chance that I will train the model again. Should I create a model that fits the data of the present size or should I create a bigger one in order to adjust future data?
I can get data from the Internet. But this question is about the approaches to take when we have a small amount of data, like the one in National Data Science Bowl (classifying Planktons).