I think the model is not overfitting. The validation loss normally decreases during the initial phase of training, as does the training loss. However, when the network begins to overfit the data, the loss on the validation set typically begins to rise and clearly there is not much difference between the training loss and validation loss. You can refer to Improve Shallow Neural Network Generalization and Avoid Overfitting for more understanding of Overfitting and steps to avoid overfitting.
The Validation accuracy can be higher than the training (mini-batch) accuracy, one possible situtation is when the network has layers that behave differently during prediction than during training for example, dropout layers. It also depends on how the training & validation data are split.
