![](bg6.png)
ral networks in fine-grained classification applications
as they contain more number of layers with more
number of trainable parameters which the model can
use for learning discriminative features allowing it to
distinguish between similar classes. Also, adding a
dense layer to the InceptionResnetv2 allows for more
features to be extracted while using L2 regularization
to prevent overfitting the training dataset.
Our future work will include training and evalu-
ating our models using (Buzzelli and Segantin, 2021)
dataset to provide us with a larger and a broader set of
annotations than the Stanford car dataset as it includes
Type-level annotations for all CompCars models.
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