Material Classification

MINC is a large-scale, open dataset of materials in the wild. Source: Material Recognition in the Wild with the Materials in Context Database Image Source: Bell et al

This dataset encompasses a diverse range of tactile features that are instrumental in bifurcating various material properties. Three downstream tasks are considered: 1) categorization of materials, 2) distinction between hard and soft surfaces, and 3) distinction between smooth and textured surfaces.

LabPics Chemistry Dataset Dataset for computer vision for materials segmentation and classification in chemistry labs, medical labs, and any setting where materials are handled inside containers. The Vector-LabPics dataset comprises 7900 images of materials in various phases and processes within mostly transparent vessels in chemistry labs, medical labs and hospitals, and other environments. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, gel, granular, vapor) . The fill level, labels, corks, and other parts of the vessel are also annotated. The material classes cover the main states of matter, including liquids, solids, vapors, foams, gels, and subcategories like powder, granular, and suspension. Relationships between materials, such as which material is immersed inside other materials, are annotated. The vessel class cover glassware, labware plates, bottles, and any other type of vessel that is used to contain or carry materials. The type of vessel (e.g., syringe, tube, cup, infusion bottle/bag), and the properties of the vessel (transparent, opaque) are annotated. In addition, vessel parts such as corks, labels, spikes, and valves are annotated. Relations and hierarchies between vessels and materials are also annotated, such as which vessel contains which material or which vessels are linked or contain each other. The images were collected from various contributors and covered most aspects of chemistry lab works as well as a variety of other fields where materials are handled in container vessels. Documents specifying annotation formats are available inside the dataset file. Version 1 contain 2200 images with simple instance and semantic annotations, and is relatively simple to use, it is described in the paper "Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set" Format The dataset contains annotated images for both material and vessels in chemistry labs, medical labs, and any area where liquids and solids are handled within vessels. There are two levels of annotation for each image. One annotation set for vessels and the second for the material phases inside these vessels. Vessels are defined as any container that can carry materials such as Jars, Erlenmayers, Tubes, Funnels, syringes, IV bags, and any other labware or glassware that can contain or carry materials. Material phases are any material contained within or on the vessel. For example, for two-phase separating liquids, each liquid phase is annotated as one instance. If there is foam above the liquid or a chunk of solid inside the liquid, the foam, liquid, and solid will be annotated as different phases. In addition, vessel parts like cork, labels, and valves are annotated as instances. For each instance, there is a list of all the classes it belongs to, and a list of its property. For vessels, the instance classes are the vessel type (Cup, jar, Separatory-funnel…) and the vessel properties (Transparent, Opaque…). For materials, the classes are the material types ( Liquid, solid, suspension, foam, powder…) and their properties (Scattered, On vessel surface…), and for parts, the part type (cork/label). In addition, the relations between instances are annotated. This includes which material instances are inside which vessels, which vessels are linked to each other or are inside each other (for vessels inside other vessels), and which material phase is immersed inside another material phase. In addition to instance segmentation maps, the dataset also includes semantic segmentation maps that give each pixel in the image all the classes to which it belongs. In other words, for each class (Liquid, Solid, Vessel, Foam), there is a map of all the regions in the image belonging to this class. Note that every pixel and every instance can have several classes. In addition, instances often overlap, like in the case of material inside the vessel, vessel inside the vessel, and material phase immersed inside other material (like solid inside liquid).

SpectroVision is a dataset of 14,400 high resolution texture images and spectral measurements collected from a PR2 mobile manipulator that interacted with 144 household objects from eight material categories. Source: https://github.com/Healthcare-Robotics/spectrovision Image Source: https://github.com/Healthcare-Robotics/spectrovision

OpenSurfaces is a large database of annotated surfaces created from real-world consumer photographs. The framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information.