CODEWITHSUNDEEP
tensorflowkerasconv-neural-network
VanodyaPerera
VanodyaPerera

Reputation: 137

Image classification model re-calibration

I built an image classification model (CNN) using tensorflow-keras. I have some new images which I need to feed into the same model in order to increase the accuracy of the existing model.

I tried using the following code. But it decreases the accuracy.

re_calibrated_model = loaded_model.fit_generator(new_training_set,
                                                   steps_per_epoch=int(stp),
                                                   epochs=int(epc),
                                                   validation_data=new_test_set,
                                                   verbose=1,
                                                   validation_steps = 50)

Is there any method that I can use to re-calibrate my CNN model?

Upvotes: 0

Views: 192

Answers (2)

Jaco Botha
Jaco Botha

Reputation: 11

What I normally do is to train the CNN model on the first batch of images and save that model. If I need to "retrain" the model with additional images, I load the previous saved model from disk and apply the inputs (test and train) and call the fit method. As mentioned before this only works if your outputs are exactly the same i.e. if you are using the same input and output classes.

In my experience training models using different image batches does not necessarily make your model more or less accurate, but rather increase the training time with each batch. Since I am using a CPU to train, my training time is about 30% faster if I train two batches of 1000 images each as oppose to training one batch of 2000 images for example.

Upvotes: 0

Uiyun Kim
Uiyun Kim

Reputation: 78

Your new training session does not start from previous training accuracy if you use completely different dataset to do second training.

You need to feed (old_images+new_images) for your intention.

Upvotes: 0

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