Solving an Image Classification Problem (Overfitting)

Working on Using ResNet, Inception V3, VGG19.

While Building an image classifier, I have trained in batch-wise. After training, I am getting accuracy on the dataset but getting poor accuracy on different images.

On my dataset validation accuracy is good and validation is also decreasing. But when I use the same model to classify images captured from a mobile phone, the accuracy is poor (3-5 out of 12 are correct).

I acquired the validation accuracy of 88%.

In the Inception V3, there's no fluctuation of accuracy and it's decreasing smoothly.

I have used Nvidia GPU.

Sample size = 4000, Training dataset = 3200

I have used 10 classes, 400 each class size.

Training loss is decreasing but the validation error is increasing.

I have performed scaling (data augmentation). I could not perform the rest of data augmentation techniques like flip, etc. Because I'm getting a memory error.

I have considered 40 epochs.

Accuracy differs while using fit() & fit_generator(). So, I have switched to shuffle = True in get_batches().

I am getting a memory error after using shuffle in fit(). I'm using keras data generator.

When I increase class size from 400 to 500, I'm facing memory error.

I want to build a classifier, no detection.

Can you solve this issue?

Image Processing Keras Machine Learning (ML) Python Tensorflow

Project ID: #18208215

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