Might still be fine. The most recent crop of vLLMs proactively use whichever programs are available on the system (e.g. ImageMagick or PIL) to "zoom in" by cropping subimages if they can't quite make out the details.
Might still be fine. The most recent crop of vLLMs proactively use whichever programs are available on the system (e.g. ImageMagick or PIL) to "zoom in" by cropping subimages if they can't quite make out the details.
Downsizing a higher res image to lower res means the zoom will be blurry.
The order is:
Then you have a separate issue where the LLM can't piece together 9 subimages well.
They process the original image file with Python on the local device. (And I've seen the web chats do this with their "computer use" features too.)
The really wild one is even blind models will do this and they'll try to run stats on the pixels to figure out what it looks like... the even wilder thing is that it kind of works!
If the API accepts only 800 by 800, the aegument youre making is "fix it in the harness".
I don't think the n by n subgrid fixes this the way most harnesses do, as it'll fail to count things if you have more overlap and fail relatiomships if you have less
That seems weirdly specific?
And if you are counting things it should be trivial to note the position of your items and not double-count them, no?
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