  We explore the field of wildlife re-identification by implementing an MLP
from scratch using NumPy, A DCNN using Keras, and a binary classifier with
LightGBM for the purpose of learning for an assignment. Analyzing the
performance of multiple models on multiple datasets. We attempt to replicate
prior research in metric learning for wildlife re-identification. Firstly, we
find that the usage of MLPs trained for classification, then removing the
output layer and using the second last layer as an embedding was not a
successful strategy for similar learning; it seems like losses designed for
embeddings such as triplet loss are required. The DCNNS performed well on some
datasets but poorly on others, which did not align with findings in previous
literature. The LightGBM classifier overfitted too heavily and was not
significantly better than a constant model when trained and evaluated on all
pairs using accuracy as a metric. The technical implementations used seem to
match standards according to comparisons with documentation examples and good
results on certain datasets. However, there is still more to explore in regards
to being able to fully recreate past literature.
