Changes

It is straightforward just by looking at the figure below that this This dataset is highly unbalanced, having a lot of samples only for two classes i.e. ''capacitor'' and ''resistor''. In this situation, it is not a good idea to use this dataset as it is, simply because the models will be trained on image batches mainly composed of the most common components, hence learning only a restricted number of features. This has as a consequence that the models will probably be very good at classifying ''capacitor'' and ''resistor'' classes and pretty bad at classifying the remaining ones. Therefore, the missing data must be increased with oversampling.

The training was done in the cloud using Google Colab. All the models were trained with the same configuration for 1000 epochs, providing at each step of an epoch a mini-batch of 32 images. The Adam was chosen as optimizer, providing an initial learn rate was initially set at of 0.0001 with and a learning rate schedule that uses an exponential decay schedule, and dropout with a decay rate of 0.96. Dropout rate was set at 0.4 for all models. Patience for early stopping was set at 100 epochs. The training images were further augmented with random zoom, shift and, rotation in order to improve model robustness on validation and test subsets and prevent the risk of overfitting.