Welcome to the documentation for MultiBench/MultiZoo, a comprehensive evaluation and set of implementations of existing multimodal machine learning algorithms. It contains 20 methods spanning different methodological innovations in (1) data preprocessing, (2) fusion paradigms, (3) optimization objectives, and (4) training procedures.
start/installation start/datadownload start/generalguide
Simple Use Case <https://colab.research.google.com/github/pliang279/MultiBench/blob/main/examples/Multibench_Example_Usage_Colab.ipynb> Multimodal Fusion Architecture Search <https://colab.research.google.com/github/pliang279/MultiBench/blob/main/examples/Multibench_Example_Usage_On_Colab_Part_2_MFAS.ipynb> MCTN <https://colab.research.google.com/github/pliang279/MultiBench/blob/main/examples/Multibench_Example_Usage_On_Colab_Part_3_MCTN.ipynb>
datasets/modules eval_scripts/modules fusions/modules objective_functions/modules robustness/modules training_structures/modules unimodals/modules utils/modules
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