Databases

A database is a collection of data files, one for each output of the database. This data are inputs to the BEAT toolchains. Therefore, it is important to define evaluation protocols, which describe how a specific system must use the raw data of a given database.

For instance, a recognition system will typically use a subset of the data to train a recognition model, while another subset of data will be used to evaluate the performance of this model.

Structure of a database

A database has the following structure on disk:

database_name/
    output1_name.data
    output2_name.data
    ...
    outputN_name.data

For a given database, the BEAT platform will typically stores information about the root folder containing this raw data as well as a description of it.

Evaluation protocols

A BEAT evaluation protocol consists of several datasets, each datasets having several outputs with well-defined data formats. In practice, each dataset will typically be used for a different purpose.

For instance, in the case of a simple face recognition protocol, the database may be split into three datasets: one for training, one for enrolling client-specific model, and one for testing these models. The training dataset may have two outputs: grayscale images as two-dimensional array of type uint8 and client id as uint64 integers.

The BEAT platform is data-driven, which means that all the outputs of a given dataset are synchronized. The way the data is generated by each template is defined in a piece of code called the database view. It is important that a database view has a deterministic behavior for reproducibility purposes.

Database set templates

In practice, different databases used for the same purpose may have the exact same datasets with the exact same outputs (and attached data formats). In this case, it is interesting to abstract the definition of the database sets from a given database. BEAT defines database set templates for this purpose.

For instance, the simple face recognition evaluation protocol described above, which consists of three datasets and few inputs may be abstracted in a database set template. This template defines both the datasets, their outputs as well as their corresponding data formats. Next, if several databases implements such a protocol, they may rely on the same database set template. Similarly, evaluation protocols testing different conditions (such as enrolling on clean and testing on clean data vs. enrolling on clean and testing on noisy data) may rely on the same database set template.

In practice, this reduces the amount of work to integrate new databases and/or new evaluation protocols into the platform. Besides, at the experiment level, this allows to re-use a toolchain on a different database, with almost no configuration changes from the user.