## 2 Materials and Methods
### Industrial Dataset  
The joint research focused on deep learning supported evaluation of a large sized, soldering-related industrial dataset, provided by Robert Bosch, Ltd. (Bosch). As a concrete industrial example, in electronics manufacturing the soldering process refers to joining two or more metallic items together by melting and putting a filler metal into the joint. This filler metal is called solder and has a lower melting point than the adjoining metals. Soldering is utilized in many household devices and in-vehicle equipment. The reliability of solder joints is generally evaluated after long environmental testing, which is part of the product development process. Several methods are used for this process, including  
Figure 1: Pipeline for domain experts to evaluate their own dataset. On the left, the first stage can be seen as the Data Preprocessing and the Pre-trained Model Selection for further evaluation. The train and test subsets are fed into the selected pre-trained model for feature extraction. The second stage is the Evaluation and Prediction, where the UMAP method is applied on the train and test sets’ representations independently, then a graph cut technique is used to define the most accurate clustering result. The next stage is the Visual Inspection by the domain experts, who supervise the results and reinforce the given labels. The final stage is Misclassification detection, where the two best performing pre-trained models classify the images and only those images that they disagree on are sent back to the domain experts for a final revision. destructive techniques such as creating polished cross-sections and evaluating the internal structure of the solder joint. This database represents cross-sections and other visual inspection images collected from different laboratories: pictures are taken under different lighting conditions, different camera parameters, and under different angles, indicating a difference in data distribution. The database does not have predefined rules about the capturing methodology. In addition, images are either captured using microscopes or ordinary photo cameras, showing electronic control units in whole or partially. These images happened to be collected over many years, are managed in a complex directory structure, and lack the proper labels. Figure 2 shows example images from the Bosch dataset representing the two main image categories: _cross-section_ samples and the so called other recordings. These two image group names refer to the target categories of the classification task. The data used in this task was randomly sampled from a set of a more comprehensive dataset (size of \(\approx\)75k). The domain experts had to randomly select images from the complete collection, which is stored in a complex directory structure. The selected data were manually renamed and labeled. This involved assigning each image one of the two given labels: either _cross-section_ or _other_. Even though there are only two distinct categories, each has multiple subclasses. The images in the other category are records of essential details and devices that are relevant in the Bosch laboratories. Such as parts and the boxes that contain the type of equipment. The cross-section images appear to have two main types: bright field and dark field. The aforementioned attributes are only general observations and these are not covering the overall characteristic of the dataset. The subset selected for the research was approximately 8% of the overall dataset. The experts have labeled 6235 images. Out of this sample set 4159 belong to the cross-section category, and 2076 to the other visual inspection-related images. This random subset was not balanced, as it contained about twice as much cross-section data as the number of images in the other category. Although, this distribution represents the state of the full dataset, where the number of cross-section recordings is roughly twice the size of the other category.