Variability in instrument calibration, both between different devices and over time, remains a significant obstacle to Raman spectroscopy. Even minor shifts in wavenumber can substantially reduce classification accuracy. Traditional spectral models struggle to remain robust under such conditions, particularly due to the one‐dimensional (1D) nature of spectral inputs and the absence of pretrained deep learning architectures for Raman spectra. In this study, we systematically investigate the robustness of a previously introduced spider plot–based spectral representation specifically with respect to wavenumber calibration drift in test data. In this approach, each Raman spectrum is converted into a radial spider plot in which the angle of each sector represents the wavenumber, and the color indicates the normalized intensity, enabling the use of powerful image‐based deep learning models. We employed EfficientNetB7, a pretrained convolutional neural network (CNN), to classify transformed spectra. Using a dataset of 5420 spectra from six bacterial species, we introduced synthetic wavenumber shifts of ±9 cm −1 to simulate realistic calibration drift in the test data. The traditional method, which combined principal component analysis with linear discriminant analysis (PCA‐LDA), showed a significant drop in balanced accuracy from 0.90 to 0.77 when shifts were present. By contrast, our spider plot–based approach retained strong performance, achieving balanced accuracy scores of 0.88 without shifts and 0.83 with shifts. Although no spectral augmentation was done in the training of either classification workflow, we attribute this robustness to the rotational consistency of spider plots under spectral shift and the tolerance to image rotation in CNNs, which are pretrained on large, augmented image datasets. Our findings demonstrate the advantage of using image‐based methods to mitigate the effects of calibration variability in Raman spectroscopy.