  The SDSS-IV dataset contains information about various astronomical bodies
such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our
work on deep multimodal learning, which utilized transfer learning to classify
the SDSS-IV dataset, we further extended our research in the fine tuning of
these architectures to study the effect in the classification scenario.
Architectures such as Resnet-50, DenseNet-121 VGG-16, Xception, EfficientNetB2,
MobileNetV2 and NasnetMobile have been built using layer wise fine tuning at
different levels. Our findings suggest that freezing all layers with Imagenet
weights and adding a final trainable layer may not be the optimal solution.
Further, baseline models and models that have higher number of trainable layers
performed similarly in certain architectures. Model need to be fine tuned at
different levels and a specific training ratio is required for a model to be
termed ideal. Different architectures had different responses to the change in
the number of trainable layers w.r.t accuracies. While models such as
DenseNet-121, Xception, EfficientNetB2 achieved peak accuracies that were
relatively consistent with near perfect training curves, models such as
Resnet-50,VGG-16, MobileNetV2 and NasnetMobile had lower, delayed peak
accuracies with poorly fitting training curves. It was also found that though
mobile neural networks have lesser parameters and model size, they may not
always be ideal for deployment on a low computational device as they had
consistently lower validation accuracies. Customized evaluation metrics such as
Tuning Parameter Ratio and Tuning Layer Ratio are used for model evaluation.
