Hi Stephen,
In my understanding you have a custom dataset and with the help of which you are trying to train a YOLO (You Only Look Once) Object detector to identify waste objects. Your dataset comprises of 1900 images and to enhance the training outcome you applied augmentation but that did not seem to improve the model’s performance significantly
Please know that with a limited dataset it is highly likely that the model will be overfit to the data if more epochs are run to train the model, applying augmentation to a dataset which is comprising of identical pictures would cause more confusion on augmentation. The aim should be to increase the dataset size by including more and different images of the objects. Since the classification is also being done for more than 2 classes an ample number of images in the dataset for each one of them would be a necessity before applying augmentation.
You can refer to the following documentation link to know more about the best practices to be followed for data augmentation:
Please refer to the following documentation to learn more about hyperparameter evolution:
Hope this helps!
