  There are many hurdles that prevent the replication of existing work which
hinders the development of new activity classification models. These hurdles
include switching between multiple deep learning libraries and the development
of boilerplate experimental pipelines. We present M-PACT to overcome existing
issues by removing the need to develop boilerplate code which allows users to
quickly prototype action classification models while leveraging existing
state-of-the-art (SOTA) models available in the platform. M-PACT is the first
to offer four SOTA activity classification models, I3D, C3D, ResNet50+LSTM, and
TSN, under a single platform with reproducible competitive results. This
platform allows for the generation of models and results over activity
recognition datasets through the use of modular code, various preprocessing and
neural network layers, and seamless data flow. In this paper, we present the
system architecture, detail the functions of various modules, and describe the
basic tools to develop a new model in M-PACT.
