The basic parts of the Glimpse library include backends, models, and worker pools. Additionally, Glimpse supports a number of simple algorithms for learning filter kernels (i.e., prototypes).
In Glimpse, a backend is an implementation of a set of low-level filtering operations, such as dot products (convolution) and radial basis functions (RBFs). Each backend uses a different technique for implementing these operations, and thus provide different performance benefits. For example, CythonBackend implements most operations in compiled C++, and is thus the default backend.
A model implements a full hierarchical network, with layer processing implemented using an arbitrary backend. Functionally, a model provides a transformation from an image to a feature vector given by the activity of the highest layer in the model. For example, in the case of an HMAX-like model , the feature vector is given by the activity of the C2 layer.
In general, however, this transformation is a mapping between arbitrary states of the network. Thus, given an input state and a description of the desired result, the transformation emits a corresponding output state. Notably, this deals with all (transitive) dependencies needed to compute the output state, provided that these dependencies are eventually satisfied. As an example, this means that a model with three layers (A, B, and C, in that order) can be used to compute layer C from any input state containing either A, B, or both A and B.
Finally, a worker pool implements a parallelization strategy for evaluating an arbitrary model on a set of images. Example strategies include multi-core processing on a single host, as well as multi-host processing on a compute cluster.