  Tiny object classification problem exists in many machine learning
applications like medical imaging or remote sensing, where the object of
interest usually occupies a small region of the whole image. It is challenging
to design an efficient machine learning model with respect to tiny object of
interest. Current neural network structures are unable to deal with tiny object
efficiently because they are mainly developed for images featured by large
scale objects. However, in quantum physics, there is a great theoretical
foundation guiding us to analyze the target function for image classification
regarding to specific objects size ratio. In our work, we apply Tensor Networks
to solve this arising tough machine learning problem. First, we summarize the
previous work that connects quantum spin model to image classification and
bring the theory into the scenario of tiny object classification. Second, we
propose using 2D multi-scale entanglement renormalization ansatz (MERA) to
classify tiny objects in image. In the end, our experimental results indicate
that tensor network models are effective for tiny object classification problem
and potentially will beat state-of-the-art. Our codes will be available online
https://github.com/timqqt/MERA_Image_Classification.
