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Question 1.- The dataset consists of 387 images. It is enough for the proper training of the net? How have you decided working in those data only and not with a different dataset?
Question 2.- Since your data consist only of a building hall. How are you avoiding over-fitting problems?
Question 3.- Given the scarcity of data. How would it generalize to other environments? Would it work in a similar corridor?
Question 4.- You have used different tools for image augmentation. How have you checked that the operations done are correct? Have you done visual verification or you used some metrics to determine which operations are valid?
Question 5.- The work is focused in a lightweight implementation. Have you worked with other implementations before considering the lightweight configuration? Howwouldit compare with more heavy networks?
computer vision and deep learning I am a student in a master degree I already run a code that is from this Github with a dataset from is a small dataset I have some question from the professor that I would like to enrich can you help as you see my deadline is in a short notice
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