Hi Wassim,
In addition to what @Prerna_Dhareshwar said, do have a look at your training data to make sure there are no biases or features in the image that would allow the network to “cheat”. One example would be ratio of hits and misses in your training data, which ideally should be 1(called a balanced dataset). Another example, if you collected the training data for “hit” during the day, training data for “miss” during the night, and all validation data during the night, your network could just be predicting day or night depending on the lighting conditions, and get 100% accuracy on your training data.
Hope this helps!