DOI: 10.57760/sciencedb.j00104.00062
title: Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification
title: Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

description: One table and six figures. Table 1 shows the number of images for each label in the 1μ–2μ data set, adopting the same labelling used in [11, 12, 13], reported here for completeness: 0 = Porous sponges, 1 = Patterned surfaces, 2 = Particles, 3 = Films and coated surfaces, 4 = Powders, 5 = Tips, 6 = Nanowires, 7 = Biological, 8 = MEMS devices and electrodes, 9 = Fibres.Figure 1 shows test accuracy as a function of the number of training epochs obtained by training from scratch Inception-v3 (magenta), Inception-v4 (orange), Inception-Resnet (green), and AlexNet (black) on SEM data set. All the models were trained with the best combination of hyperparameters, according to the memory capability of the available hardware. In Figure 2, Main: Test accuracy as a function of the number of training epochs obtained when fine tuning on the SEM data set Inception-v3 (magenta) and Inception-v4 (orange) starting from the ImageNet checkpoint, and Inception-v3 (blue) from the SEM checkpoint that, as expected, converges very rapidly. Inset: Test accuracy as a function of the number of training epochs obtained when performing feature extraction of Inception-v3 (magenta), Inception-v4 (orange), and Inception-Resnet (green) on the SEM data set starting from the ImageNet checkpoint. All the models were trained with the best combination of hyperparameters, according to the memory capability of the hardware available. Figure 3 shows intrinsic Dimension of the 1μ–2μ_1001 data set, varying the sample size, computed before autoencoding (green lines) and after autoencoding (red lines). The three brightness levels for each color correspond to the percentage of points used in the linear ﬁ t: 90%, 70%, and 50%. Figure 4 shows ddisc heatmap for a manually labelled subset of images. Figure 5 presents heatmaps of the distances obtained via Inception-v3. The image captions specify the methods used and indicate the correlation index with ddisc. Figure 6 shows NMI scores of the clustering obtained by the five hierarchical algorithms (solid lines) considered as a function of k, the number of clusters. The scores of the artificial scenarios are reported as orange (good case) and green (uniform case) dashed lines.
description: One table and six figures. Table 1 shows the number of images for each label in the 1μ–2μ data set, adopting the same labelling used in [11, 12, 13], reported here for completeness: 0 = Porous sponges, 1 = Patterned surfaces, 2 = Particles, 3 = Films and coated surfaces, 4 = Powders, 5 = Tips, 6 = Nanowires, 7 = Biological, 8 = MEMS devices and electrodes, 9 = Fibres.Figure 1 shows test accuracy as a function of the number of training epochs obtained by training from scratch Inception-v3 (magenta), Inception-v4 (orange), Inception-Resnet (green), and AlexNet (black) on SEM data set. All the models were trained with the best combination of hyperparameters, according to the memory capability of the available hardware. In Figure 2, Main: Test accuracy as a function of the number of training epochs obtained when fine tuning on the SEM data set Inception-v3 (magenta) and Inception-v4 (orange) starting from the ImageNet checkpoint, and Inception-v3 (blue) from the SEM checkpoint that, as expected, converges very rapidly. Inset: Test accuracy as a function of the number of training epochs obtained when performing feature extraction of Inception-v3 (magenta), Inception-v4 (orange), and Inception-Resnet (green) on the SEM data set starting from the ImageNet checkpoint. All the models were trained with the best combination of hyperparameters, according to the memory capability of the hardware available. Figure 3 shows intrinsic Dimension of the 1μ–2μ_1001 data set, varying the sample size, computed before autoencoding (green lines) and after autoencoding (red lines). The three brightness levels for each color correspond to the percentage of points used in the linear ﬁ t: 90%, 70%, and 50%. Figure 4 shows ddisc heatmap for a manually labelled subset of images. Figure 5 presents heatmaps of the distances obtained via Inception-v3. The image captions specify the methods used and indicate the correlation index with ddisc. Figure 6 shows NMI scores of the clustering obtained by the five hierarchical algorithms (solid lines) considered as a function of k, the number of clusters. The scores of the artificial scenarios are reported as orange (good case) and green (uniform case) dashed lines.