Effective Train/Test Stratification for Object Detection

Fri, 30 Sep 2022 19:47:39 +0530

TL;DR: Here’s a talk based on this post:

There’s an unavoidable, inherent difficulty in fine-tuning deep neural networks, which stems from the lack of training data. It would seem ridiculous to a layperson that a pretrained vision model (containing millions of parameters, trained on millions of images) could learn to solve highly specific problems. Therefore, when fine-tuned models do perform well, they seem all the more miraculous. But on the other hand, we also know that it is easier to move from the general to the specific, than the reverse. Specialization after generalization is easier than the reverse.

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Effective Train/Test Stratification for Object Detection