Divide your data to 80% of training data and 20% of evaluation data (some people use 90%-10%). To evaluate your model compare the prediction of the model and the label given to you for every image in the evaluation data, if it’s equal add 1 to a correct_counter. After running over all the example in the evaluation data, just make the following division: correct_counter/size(evaluation).
This is probably the most basic method to evaluate a model.