In recent years, optical fiber sensors based on multimode interference (MMI) have attracted increasing interest and developed into various sensors used in many practical applications. This review presents MMI-based fiber sensors with a specific focus on the probe structures, measurement methods, and sensing properties of different structures. The fundamentals of MMI-based fiber sensors are briefly described. Further, five main categories of SMS structure-based fiber sensors are reviewed, including conventional SMS fiber sensors, MMI-based sensors with no-core fiber (NCF), MMI-based sensors with etched/polished fiber, MMI-based sensors with tapered fiber, and cascades of MMI-based fiber sensors. Besides, their probe structures, sensing properties, practical fields, and measurement sensitivities are summarized. Finally, the current challenges and technique outlook of the MMI-based fiber sensors are pointed out, which gives the potential solutions for the existing problems and future development. This work indicates that the MMI-based fiber sensors bring the possibilities of applying multi-mode fiber (MMF) in different measurement fields with high sensitivities, easy fabrication, and low cost. With the appearance and development of new SMS fiber sensors, it will contribute significant value to scientific research and industrial applications.

2D materials and their heterostructures are prominent for fabricating next‐generation optical and photonic devices. The optical, electrical, and mechanical properties of 2D materials largely depend on atomic layer numbers. Although machine learning techniques are implemented to identify large‐area thickness distribution using microscopic images, the existing work mainly focuses on rough identification of thicknesses with in‐house datasets which limits fair and comprehensive comparisons of new machine learning approaches. Here, first a microscopic dataset is collected and released for three fundamental image processing tasks including multilabel classification, segmentation, and detection. Then three deep‐learning architectures DenseNet, U‐Net, and Mask‐region convolutional neural network (RCNN) are benchmarked on three tasks and their robustness is evaluated on the augmented 2D microscopic images with different optical contrast variations. Deep learning models are trained and evaluated to identify mono‐, bi‐, tri‐, multilayer and bulk flakes using microscopic images of MoS₂ fabricated on the SiO₂/Si substrate by chemical vapor deposition. The relation between model performances and statistics of datasets is studied based on the international commission on illumination (CIE) 1931 color space and red, green, blue (RGB) histograms of optical contrast differences. Finally, the robust pretrained models are integrated into a graphic user interface for the on‐site use of full field‐of‐view images captured by bright‐field microscopes.