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Comment by adastra22 | Hacker News Reader

They’re not talking about zooming, hence the quotes.

Yes. When the LLM tries to read an image, it will be resized by DeepSeek's server to 800x800, which might be a bit blurry. The LLM will then crop a smaller image from the high resolution image (using e.g. the `convert` tool via bash) and will then read the small cropped image. This image will still be resized to 800x800 by DeepSeek's server, but since it is already small, there is no or little loss of quality.

If the harness does it that's just like saying "please use a workaround". You'll lose fidelity and LLMs will lose the ability to count things or maintain relationships for schematics, etc

LLMs read images by splitting them up into e.g. 16x16 patches, which are then converted to embedding vectors and fed to the LLM, so from a technical point of view, feeding a big image as many 20x20 patches all at once is not too different from cropping subimages from the image, splitting those subimages into patches and feeding them to the LLM. Of course, the LLM has to be trained to understand that those images belong together, but it can be done.

You say "it can be trained" but they fail at counting in my use cases, let alone maintaining symbolic relationships.

Can you point me at one that can understand a detailed block diagram? Frontier is fine, soliciting recommendations

For counting, there are specialized counting models, e.g. https://huggingface.co/spaces/MengqiLei/count-anything-demo

I tried to parse hand-drawn ER diagrams in the past and did not have much success with any model, frontier or otherwise. If you have annotated data, I'd recommend finetuning a recent (dense) VLM, but don't expect 100% accuracy. https://unsloth.ai/docs/basics/vision-fine-tuning

good advice; tbh I'm really trying to use counting as a proxy for "understand symbol, flag annotation next to it, associate annotation with symbol and store as object in location".

Ideally, look at second diagram and see something similar in the same location and understand it's the same physical object.

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