I understand that you want to boost the accuracy for the second and third class where dataset being used is imbalanced. There are a few ways to improve the performance of the model:
- SMOTE (Synthetic Minority Over-sampling Technique) is a popular data augmentation technique for imbalanced datasets. It generates synthetic samples for the minority class (second and third classes in this case) by interpolating between existing samples. You can refer to Synthetic Minority Over-sampling Technique (SMOTE) file exchange to download the GitHub repository containing the implementation for the algorithm.
- Using a Weighted Loss function: The loss function can be weighted to give more importance to the minority class by multiplying the loss of the positive examples by a weight factor. To achieve this, you can try modifying the parameters for “crossentropy” defined in “objectnessLoss” and “classConfidenceLoss” functions as given in Object Detection Using YOLO v3 Deep Learning. The “perfweights” parameter as explained in Cross-Entropy, lets you modify the weights for each class, thereby giving more priority to the second and third classes.
