The recent advancement in deep learning models has been largely attributed to the quantity and diversity of data gathered in recent years. Similarly, for deep learning image-based classification tasks, for a particular problem, to make your model robust to any input data concerned to your problem, you have to create additional data with a variety of it, and comes the role of data augmentation. Your preparation(data augmentation) includes daily running, proper diet, extensive workout and so on. To conquer this competition, you need to prepare very hard. You can easily relate this relation like, say, you (the DL model) have participated in a long run competition of about 200M. The performance of any supervised deep learning model is highly dependent on the amount and diversity of data being fed to the model.
0 Comments
Leave a Reply. |