Deep_Object_Pose VS Weaviate

I've been doing some research in this area and there are a few deep learning solutions to this problem. For example, NVIDIA's Deep Object Pose Estimation will estimate the 6DOF pose of a known object. But you'll have to train the network if you want to detect a new object. PoseCNN, which someone else mentioned, does a similar thing. CenterPose is more interesting, as it can estimate then pose of an object from a known category; e.g. sneakers, or laptops, rather that one specific object (as DOPE and PoseCNN do).

For our last event of ArchE Week, the Ohio State Underwater Robotics Team (Website, Instagram) is hosting a workshop tonight on machine learning! The workshop is an interactive walkthrough of using machine learning solutions to make predictions. Some example problems we could be trying to solve are predicting a grade, predicting the weather, and the classic recognize a digit problem. Our team personally uses machine learning to do real-time object detection with YOLO and NVidia DOPE, so we may touch on that as well!

In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.

This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424

Link to GitHub -->

Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:

1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.

2. Parameter tuning. lowering `efConstruction` can speed up imports.

3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.

In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.