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First of all:

• I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512, try this.

• Try to make your batch size 30, and decrease number of epochs to nearly 10 or 20, 100 epochs is too many for your small size dataset.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale,...

2- Using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.

3- One of the good techniques in your case is to do early stopping, in any epoch when you see that the model goes to overfit, stop it.

4- Using cross-validation to train/test your model.

and many more...

• for more, see here, or here, or here.

feel free to ask any questions

First of all:

• I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512. Try this.

• Try to make your batch size 30, and decrease number of epochs to nearly 10 or 20. 100 epochs are too many for your small size dataset.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale, etc.

2- Using regularization techniques like dropout (you already did it), but you can play with dropout rate. Try more or less than 0.5.

3- One of the good techniques in your case is to do early stopping. In any epoch when you see that the model goes to overfit, stop it.

4- Using cross-validation to train/test your model.

and many more.

• For more, see here, or here, or here.

Feel free to ask any further questions.

First of all:

-I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512, try this.

-try to make your batch size 30, and decrease number of epochs to nearly 10 or 20, 100 epochs is too many for your small size dataset.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale,...

2- Using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.

3- One of the good techniques in your case is to do early stopping, in any epoch when you see that the model goes to overfit, stop it.

4- Using cross-validation to train/test your model.

and many more...

• for more, see here, or here, or here.

feel free to ask any questions

First of all:

• I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512, try this.

• Try to make your batch size 30, and decrease number of epochs to nearly 10 or 20, 100 epochs is too many for your small size dataset.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale,...

2- Using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.

3- One of the good techniques in your case is to do early stopping, in any epoch when you see that the model goes to overfit, stop it.

4- Using cross-validation to train/test your model.

and many more...

• for more, see here, or here, or here.

feel free to ask any questions

First of all, I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512, try this.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale,...

2- using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.

3- using cross-validation to train/test your model.

and many more...

• for more, see here, or here, or here.

feel free to ask any questions

First of all:

-I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512, try this.

-try to make your batch size 30, and decrease number of epochs to nearly 10 or 20, 100 epochs is too many for your small size dataset.

Secondly, there is more than one way to reduce overfitting:

1- Enlarge your data set by using augmentation techniques such as flip, scale,...

2- Using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.

3- One of the good techniques in your case is to do early stopping, in any epoch when you see that the model goes to overfit, stop it.

4- Using cross-validation to train/test your model.

and many more...

• for more, see here, or here, or here.

feel free to ask any questions