Visual Transformers for Primates Classification and Covid Detection

When working with the dataset, we noticed an imbalance across the classes, in the train-We apply the vision transformer, a deep machine learning model (Figure 1a) as well as in the provided devel-dataset. The counts build around the attention mechanism, on mel-spectrogram per sample and class are similar in train & devel (c.p. Figure 1a), representations of raw audio recordings. When adding melbased but we noticed slight variations in audio-sample length (number data augmentation techniques and sample-weighting, we of frames, c.p. Figure 1b & 1c). Distributions for train and devel achieve comparable performance on both (PRS and CCS challenge) are comparable while the test dataset has variations in sample tasks of ComParE21, outperforming most single model length regarding the number of very small (<= 0.3 seconds).