A model is overfit it fits too closely to a training set of data, this resuts in the model not being abe to preform its job well on new images. If your mode has low error rates, and high variance, these are good signals of overfitting a model. To prevent this you should set aside a portion of testing images that are differnt from the rest to "test" for overfitting. If the images for training have a lower error rate, and the test images have a high error rate then your model is most likely overfitted. This means you might need to tailor your training!

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