Best way to implement 10-fold cross validation for CNN model I was performing a binary classification problem with 15000 RGB images using a scratch build CNN model. While it comes to evaluate the model, I can do it in two ways:

Splitting data Train and Test and use 10 fold cross-validation for the training data. Later with the best model, I would use the unseen Test data. In this way I got appx. 91.5% avg. accuracy for both test and validation.
Just use 10 fold cross-validation and got 92.5% avg accuracy(slightly better result than the previous one.)

Which option would be the best for reporting the performance of my model in the research article?
TIA
In my point of view, it should be Option-I
 Primary logic is to test your Model on unseen data.
Reason for that is,
When you executed your K-Fold and let's say got a score ~87% and then you tweaked your Hyperparameters.
In this way, we actually leak the test data(K-Fold test set) information to the learning process, and eventually, the process will overfit to the test data too if this hit-try happens multiple times.
So, the Option-I score should be closer to the score on future new data.