Cool to see a vector search application that leverages traditional image features. I'm curious to know which of these methods performed best on cropped images, which remain somewhat of a challenge for traditional classification models (contrastive models trained specifically with image crops tend to work much better).
Also for the vector database itself, have you considered spinning up your own open-source alternative such as Milvus (https://github.com/milvus-io/milvus), or were you only considering managed services?
The full cropped results are in the "Correctness" tab of the Google sheet linked at the bottom, with more details in the "Scoring" tab, but TL;DR the intensity vector worked best, with Goldberg (the one I chose) a pretty close second. Goldberg correctly returned the correct result highest-scored in 79% of cases, with it present in the result in 90%.
I'm primarily interested in managed services. I've been an SRE and I hope to not be in that role again.
Also for the vector database itself, have you considered spinning up your own open-source alternative such as Milvus (https://github.com/milvus-io/milvus), or were you only considering managed services?