###### Abstract
CNNs use successive convolution and pooling operations on an input image. The convolution product between the input image and a kernel extracts features from the input [16]. Then, the result, the feature map, is operated by a pooling layer to capture the essential features which describe the object of interest. This operation produces spatial reduction and a loss of object position information. Moreover, a fully connected network with non-linear activation functions is connected at the end to make predictions, and the whole network is trained by a backpropagation algorithm [17]. CNN constitutes the \"fundamental blocks\" of more advanced CNN-based architectures for computer vision applications [18, 19]. It is well known that training a CNN from scratch is energy-intensive and requires huge labeled datasets [20, 21]. That is why, to overcome this problem, it is established in the computer vision community to use transfer learning techniques [22, 23, 24]. Transfer learning  
Figure 1: Constitution of a diamond grinding wheel (DGW) (**a**) and its abrasive layer (**b**). Phenomena involved during abrasive machining: the quantification of abrasive power remains a challenging, opening research questions. consists of using a pre-trained CNN model [25] (often called \"backbones\"), trained over a large standardized dataset model by shifting its weight over a new training dataset of interest (i.e., DGW images). For example, in computer vision (especially segmentation tasks), ImageNet [26] is a widely used dataset for a pre-trained model of over 14 million labeled images. Based on this observation, several new CNN architectures have been developed over the years, such as AlexNet [27], VGG [28], ResNet [29], GoogLeNet [30], U-Net [31], Inception [32], DenseNet [33] and EfficientNet [34]. In this work, we implement a U-Net architecture for the semantic segmentation of diamond grits, using ResNet34 backbones trained on ImageNet as an encoder. However, although transfer learning is efficient, with more than 8600 hyperparameter (HP) combinations, it is impossible to manually find the optimal configuration of HPs. Some examples of hyperparameters include the model architecture and training parameters such as epoch number, batch size or learning rates. For that reason, finding the best combination of hyperparameters remains challenging even for experts in the field. In this respect, we use an Automated Machine Learning (AutoML) [35] methodology by leveraging Bayesian Hyperparameter Optimization (BO) [36; 37; 38; 39; 40] and Hyperband [41] algorithms. Finally, our work is part of an ongoing effort to make deep learning models more accurate, reliable and easier to use for researchers, engineers and programmers. The key contributions of this article include:  
* A practical application and implementation of state-of-the-art (SOTA) deep learning computer vision algorithms for semantic segmentation of objects in images in the industrial field of DGW manufacturing. * An assessment of segmentation performances of the corresponding models over state-of-the-art metrics. * To the best of our knowledge, this work is the first published attempt of using a U-Net deep learning model for automatic segmentation of diamond grits from DGW images. * The foundations of upcoming data-driven methods for DGW characterizations. * A unified methodology for automatic configuration/calibration of deep learning models by leveraging Bayesian optimization and Hyperband algorithms, an AutoML framework for making deep learning approaches easier to use. The remainder of this paper is organized as follows. Section 2 introduces the implemented U-Net deep learning architecture and the Bayesian and Hyperband algorithms, including the overall hyperparameter optimization strategy. Section 3 presents the data acquisition, software and hardware specifications, with an emphasis on manual hyperparameter complexity reduction. Section 4 provides the experimental results in two parts: Bayesian optimization experiments and diamond grit segmentation results.