Open Source Ibot: Image Bert Pre-training With Online Tokenizer

Posted on Dec 29

• Originally published at paperium.net

Imagine a system that learns to fill in missing parts of a picture, like your brain guessing a face behind a mask. That's iBOT, a simple idea that lets a model learn visual meaning from parts of images. Instead of using a fixed vocabulary, iBOT builds an online tokenizer as it learns, so the model and its teacher teach each other at the same time. The result is a model that gets very good at recognizing photos, reaching high scores on large image tests like ImageNet accuracy, and it stays strong when images are noisy or damaged. This method also helps the model find small object details, so it does well on tasks like spotting things, cutting them out, or labeling parts of a scene. It sounds complex but works like practice: mask parts, guess them back, and slowly learn what matters. The training is simpler too, no long separate setup, just one system learning together — and it makes computers see more like we do, with more robust understanding, even when images are messy.

Read article comprehensive review in Paperium.net: iBOT: Image BERT Pre-Training with Online Tokenizer

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Source: Dev.to