OML 3.1.0

The update focuses on several components:

We added "official" texts support and the corresponding Python examples. (Note, texts support in Pipelines is not supported yet.)
We introduced the RetrievalResults (RR) class — a container to store gallery items retrieved for given queries. RR provides a unified way to visualize predictions and compute metrics (if ground truths are known). It also simplifies post-processing, where an RR object is taken as input and another RR_upd is produced as output. Having these two objects allows comparison retrieval results visually or by metrics. Moreover, you can easily create a chain of such post-processors.
- RR is memory optimized because of using batching: in other words, it doesn't store full matrix of query-gallery distances. (It doesn't make search approximate though).
We made Model and Dataset the only classes responsible for processing modality-specific logic. Model is responsible for interpreting its input dimensions: for example, for images or for sequences like texts. is responsible for preparing an item: it may use for images or for texts. Functions computing metrics like , , , and other classes and functions are unified to work with any modality.

The update focuses on several components:

We added "official" texts support and the corresponding Python examples. (Note, texts support in Pipelines is not supported yet.)
We introduced the RetrievalResults (RR) class — a container to store gallery items retrieved for given queries. RR provides a unified way to visualize predictions and compute metrics (if ground truths are known). It also simplifies post-processing, where an RR object is taken as input and another RR_upd is produced as output. Having these two objects allows comparison retrieval results visually or by metrics. Moreover, you can easily create a chain of such post-processors.
- RR is memory optimized because of using batching: in other words, it doesn't store full matrix of query-gallery distances. (It doesn't make search approximate though).
We made Model and Dataset the only classes responsible for processing modality-specific logic. Model is responsible for interpreting its input dimensions: for example, for images or for sequences like texts. is responsible for preparing an item: it may use for images or for texts. Functions computing metrics like , , , and other classes and functions are unified to work with any modality.

open-metric-learning