  Transformer-based models, such as the Vision Transformer (ViT), can
outperform onvolutional Neural Networks (CNNs) in some vision tasks when there
is sufficient training data. However, (CNNs) have a strong and useful inductive
bias for vision tasks (i.e. translation equivariance and locality). In this
work, we developed a novel model architecture that we call a Mobile fish
landmark detection network (MFLD-net). We have made this model using
convolution operations based on ViT (i.e. Patch embeddings, Multi-Layer
Perceptrons). MFLD-net can achieve competitive or better results in low data
regimes while being lightweight and therefore suitable for embedded and mobile
devices. Furthermore, we show that MFLD-net can achieve keypoint (landmark)
estimation accuracies on-par or even better than some of the state-of-the-art
(CNNs) on a fish image dataset. Additionally, unlike ViT, MFLD-net does not
need a pre-trained model and can generalise well when trained on a small
dataset. We provide quantitative and qualitative results that demonstrate the
model's generalisation capabilities. This work will provide a foundation for
future efforts in developing mobile, but efficient fish monitoring systems and
devices.
