## 1 Introduction  
In recent years, different investigations have been conducted on the identification and classification of material microstructures. Studies can be found with different approaches and results based on classical machine-learning algorithms and neural networks applied to optical microscopy analysis or SEM images. The random forest technique is used as the base algorithm to obtain classifiers in the experiments performed. Thus, [PERSON] et al. [1] developed an analysis of steel images and classified ferrite-pearlite, ferrite-pearlite-bainite and bainite-martensite microstructures. In a subsequent publication, [2], the selection of models was improved by extracting different statistical attributes from the dataset, thereby, obtaining a precision of 90%. [PERSON] et al. [3] studied the classification by using a support vector machine algorithm, SVM, of pearlite, martensite, transformed pearlite, debris of cementite, residual austenite and upper and lower bainite by using SEM images. Their results showed an acceptable accuracy of 82%. They also implemented an inference method for obtaining the area (%) that the corresponding phase occupies, finding, by the end of the experiment, an accuracy of 89%. Other studies have directly addressed the problem of image segmentation with an optical image dataset. Thus, [PERSON] et al. [4] identified ferrite, pearlite and martensite by using a convolutional neural network, CNN, to obtain parameters and the simple linear iterative clustering algorithm, SLIC, for segmentation. They achieved efficient performance. Other approaches to the classification problem can be found from images taken from optical microscopy. [PERSON] et al. [5] investigated a system for automatically estimating the steel quality level with a model based on the VGG16 neural network [6] and obtained a success rate of 92.5%. Regarding datasets that consist of images obtained with an electronic microscope (SEM, EBSD), several works can be found, such as [PERSON] et al. [5], where kernel-averaged misorientations were used as parameters to classify bainite and martensite with the classical SVM and random-forest algorithms, achieving acceptable results. Different works with these types of images have created models with deep-learning techniques, such as that of [PERSON] et al. [7] where eight types of steel microstructures were classified: upper bainite, martensite and their hybrid structures. They used the pre-trained network ResNet50 as a classifier, obtaining accuracies of up to 97% using a voting method to analyze the results by means of a local interpretable model-agnostic explanation, LIME. [PERSON] et al. [8] established a comparative method of two algorithms for feature extraction, one with VGG16 pre-trained with ImageNet and another one with a gray-level co-occurrence matrix, GLCM. They concluded that the best combination for classification was VGG16 as a feature extractor with an SVM classifier. In this case, their research was applied to hot stamping ultra-high-strength steels. A segmentation technique with electronic images was addressed by [PERSON] et al. [9], where, through the use of U-NET [10], they searched for the detection of pearlite islands in biphasic ferrite-pearlite steels, reaching estimates between 79% for EBSD images and 87% for SEM images. Likewise, [PERSON] et al. [11] dealt with the subject of segmentation with U-NET; however, in this case, the different constituents involved were identified using the band contrast, BC, grain boundary misorientations and kernel average misorientation (KAM) maps. [PERSON] et al. [12] researched a different approach to steel image segmentation by working with the VGG16 pre-trained network and PixelNet [13] over the Ultra High Carbon Steel Dataset [14]. Rather than training two separate CNNs, they demonstrated that introducing a single CNN in a multi-task setting was more appropriate. Thus, microstructures were mapped to a common numerical representation before the corresponding classification of microconstituents.