MMIFR: Multi-modal industry focused data repository

In the rapidly advancing field of industrial automation, the availability of robust and diverse datasets is crucial for the development and evaluation of machine learning models. The data repository consists of four distinct versions of datasets: MMIFR-D, MMIFR-FS, MMIFR-OD and MMIFR-P. The MMIFR-D dataset comprises a comprehensive assemblage of 5907 images accompanied by corresponding textual descriptions, notably facilitating the application of industrial equipment classification. In contrast, the MMIFR-FS dataset serves as an alternative variant characterized by the inclusion of 129 distinct classes and 5907 images, specifically catering to the task of few-shot learning within the industrial domain. MMIFR-OD is another alternative variant, comprising 8,839 annotation instances across 128 distinct categories, is predominantly utilized for object detection tasks. Additionally, the MMIFR-P dataset consists of 142 textual-visual information pairs, making it suitable for detecting pairs of industrial equipment. Furthermore, we conduct a comprehensive comparative analysis of our dataset in relation to other datasets used in industrial settings. Benchmark performances for different industrial tasks on our data repository are provided. The proposed multimodal dataset, MMIFR, can be utilized for research in industrial automation, quality control, safety monitoring, and other relevant domains. • MMIFR is a multimodal dataset for industrial equipment classification and few-shot learning. • It includes four components: MMIFR-D (5907 images with textual descriptions), MMIFR-FS (129 classes for few-shot learning), MMIFR-OD (8839 instances within 128 classes), and MMIFR-P (142 textual-visual information pairs). • Comparative analysis shows MMIFR’s advantages over other industrial datasets. • Benchmark performances for various industrial tasks are provided. • MMIFR is valuable for research in industrial automation, quality control, and safety monitoring.