Is it possible to apply sklearn evaluation metrics such as precision, recall, f1_score on my problem?
Yes you can do it as long as your model is giving out either the class labels or probabilities as it predictions.
If your model is predicting the encoded (integer) labels then you can use
sklearn.metrics.precision_score(y_true, model.predict(test_x))
On the other hand if the  model is predictiong the probabilies which is norammly the case then you have to fist convert them into class labels using argmax. So if you have a batch of test_x data then you can use
sklearn.metrics.precision_score(y_true, np.argmax(model.predict(test_x), axis=1))
My deep learning topic is classifying images into 5 different categories. I used the ImageDataGenerator library to split my dataset into train and test.  I've successfully developed a model architecture following the CNN method and evaluated the performance of my model on a test dataset, which gave me an accuracy of 83%.
Is it possible to apply sklearn evaluation metrics such as precision, recall, f1_score, etc. to evaluate my test results? If yes, how can I do it?