fhaltmayer

Unfortunately that visualization doesn't really work with vector dbs. Vector dbs normally split their data into separate segments and build indexes on them separately. There is no one overarching index but rather many small ones that are searched in parallel. In addition to this, such a high compression to reduce it down to 2d/3d ends up becoming a giant blob without too much information.

You can take a look here: https://github.com/zilliztech/VectorDBBench

It lets you run you run the benchmarks using your own API keys. Although it is made by Zilliz (maintainers of Milvus), you can take a look and see what is going on and judge if its fair.

How is this different than ANNOY?

Useful link if anyone wants to keep track of all these layoffs going on: https://layoffs.fyi

With this focus on chipmaking by many nations, do people think prices will ultmately reduce or will prices be propped up by further advances?

The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.

Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.

The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.

The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).

Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.

Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....

Disclaimer: I am a part of those two open source projects.

The terraform changes just seem so unprofessional and a prime example of virtue-signaling.

No, I feel like the time it takes to learn a new layout can be used to improve skills on your current layout. Are we really chasing WPM these days?