Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours search.

Now the next task, where I am facing a problem is to find a clustering algorithm that uncovers a set of clusters using the embedding vectors generated by the BYOL trained feature extractor [dimension of the vector is 1024 & there are 1 million vectors]. I have no information apriori about the number of classes i.e. clusters in my dataset & thus cannot use Kmeans. Is there any scalable clustering algorithm that can help me uncover such clusters. I tried to use FISHDBC but the repository does not have good documentation.