The curve shows that training and validation accuracy rose steadily until the end, at around 0.90 and ~0.88–0.89, respectively, while both losses fell consistently and remained close to each other. This pattern indicates good model generalization without signs of overfitting.
The model has performed well with the majority of predictions correct in all classes (diagonally dominant), especially for blackheads, cysts, pustules, and whiteheads; however, there is still a major weakness in distinguishing papules from pustules (the largest misclassification is papules→pustules). This means that generalization is solid, but decisions on two similar classes are still vulnerable.