An analysis of three different encoder-decoder models is presented in this work. All models feature a CNN encoder based on EfficientNet and a decoder based on the Transformer model . The first model uses the original implementation from our recent publication . It contains an EfficientNetV2-M model as an encoder and a Transformer model as a decoder. The second model uses an EfficientNetV1-B7 encoder and a Transformer decoder. For the third model, EfficientNetV2-M was used as the encoder. In models 2 and 3, only the decoder part of the Transformer model was utilised, while model 1 uses the complete Transformer model. The Transformer models used have six decoder layers, eight attention heads, and an embedding dimension of 512 parameters. A detailed summary of these models can be seen in Table . All three models were implemented using Python and TensorFlow. Among them, the best-performing model was selected as the final model (see Table ).