The models were trained using TensorFlow version 2.13.0. The training process utilised the model 3 implementation, consisting of an encoder with an EfficientNetV2-M model employing default configurations and a transformer decoder with 6 layers (refer to Figure ). These models underwent training for 25 epochs on a TPU V4-128 pod slice. Training employed focal loss and the Adam optimizer, complemented by a custom schedule for the learning rate, as specified in the original transformer paper . A dropout rate of 0.1 was also used. To ensure compatibility with the encoder's settings, the images were preprocessed to attain a size of 512 x 512 before being fed into the encoder.