AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
This provides an easy-to-use tool for practitioners handling multimodal data, though it is incremental as it builds on existing AutoML and foundation model concepts.
AutoGluon-Multimodal (AutoMM) is an AutoML library for multimodal learning that simplifies fine-tuning of foundation models with minimal code, achieving superior performance in basic tasks and competitive results in advanced ones compared to existing tools.
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities spanning classification, regression, object detection, semantic matching, and image segmentation. Experiments across diverse datasets and tasks showcases AutoMM's superior performance in basic classification and regression tasks compared to existing AutoML tools, while also demonstrating competitive results in advanced tasks, aligning with specialized toolboxes designed for such purposes.