Thanks! Definitely a work in progress but getting better by the day.
We have run tests with image-detection to try to categorize products. We currently do search based on the name, description, price, store, and brand.
The problem with image-detection is cost. Given the size of our data set, it's very costly to run 800m - 1b images through a model (i.e. most products have 4 - 5 images). We've considered only doing the first 'hero' image to start though. Open to any cost-effective ideas though.
For example, if you search for "wooden chair", it would be nice to select a filter for 'category' to narrow down if I want to see "office furniture", "dining room", or "art".
I found some things on Github you could use, I'm not a dev myself and I'm not sure how scalable these are, but have a look, maybe there's something useful. https://github.com/jhc13/taggui
The category filtering is what I wanted to get at, I think the search would improve a lot.
We have run tests with image-detection to try to categorize products. We currently do search based on the name, description, price, store, and brand.
The problem with image-detection is cost. Given the size of our data set, it's very costly to run 800m - 1b images through a model (i.e. most products have 4 - 5 images). We've considered only doing the first 'hero' image to start though. Open to any cost-effective ideas though.
For example, if you search for "wooden chair", it would be nice to select a filter for 'category' to narrow down if I want to see "office furniture", "dining room", or "art".
https://www.searchagora.com/search?query=wooden+chair&count=...