Comment by bearjaws
1 day ago
It is funny to me seeing Sol used for what a "traditional" AI model can do already (counting pills).
We have vision models for our pharmacy and I could never imagine taking the latency hit to use a Sol in our robotics, it would be likely 25-50x slower.
Hi! I’m the author of this blog.
I’m evaluating these VLMs to figure out which ones are good enough to auto-annotate my data, so I can fine-tune my detector.
I wrote a bit more about this here: https://x.com/skalskip92/status/2080334344061694429?s=20
Did you evaluate any that could be self-hosted (or at least ow models), if so which one is the best you seen?
Take a look here: https://playground.roboflow.com/evals. We have few ~30B.
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Agreed, this like asking a chainsaw to carve a wooden spoon. Impressive it can, but definitely not the right tech to scale.
LLM needs to setup an image classifier to use as a tool call.
Building a dataset is expensive, manual annotation is expensive. Datasets don't exist in every niche.
I remember around 2013-15 people were scoffing at uses of deep learning CNNs for various things, because why don't you just use an SVM on HOG features? Or face detection is solved, just use Viola-Jones.
What if you give the benefit of doubt and assume the author knows about alternatives and uses VLMs for their strengths? They use it to auto-annotate training data for regular deep learning models.
Now maybe, but the gap is closing.
How are we supposed to pay off all these data centers and chips if you’re not willing to burn a microwave burrito worth of electricity for each prescription? Think of the benchmarks