I haven't tried playing with the optimizer, thank you for the notice, also thanks for the code, will try to play around with it too :)

So I import the model and unfreeze it immediately and just add my top layers ?

They're pictures of some plant, 8 classes for 8 different species of the same type of the plant.

The problem with efficient nets is that I ran a test on some models apriori, I got this graph, note that the dataset was ran for 3 epochs only each model.

https://drive.google.com/file/d/1OyXaWg6vMirYeI9zLSeGJ2v_qCz3msu4/view?usp=share_link

Oh no, from the comments i realized that I have explained things in a bad way, I train the FC layer until it early stops while the DenseNet is frozen, then I take that model and retrain the weights with unfreezing 200-ish layers and lowering the learning rate

In the post I said I unfroze the CNN layers, I meant after the transfer learning part. I run it untill it early stops with all CNN layers frozen, then run it with unfreezing the top 200 layers or so.

I'm obliged to work on Keras K don't know if it has an LR sched method, I'll check the API great advice.

Hahhaha, definitely! The pictures are of leaves of 8 different species and they're square 96-pixel images, so not so great to visually look at

Actually the resizing really boosted the performance by like 5%, I'm at at 80% now, but still looking to boost it up

1- I'm doing a 20% split, so in total they're around 2800, 700 training and validation.

2- batches of 8, Adam with LR=0.001 in the transfer part, LR=0.0001 in the fine tuning, any other combination caused everything to crumble.

3- currently 0.3, 0.5 caused some early stopping problems, since the model was stuck

4- valid_data_gen = ImageDataGenerator(rescale=1/255.)

train_data_gen = ImageDataGenerator(

rescale=1/255.,

rotation_range = 30,

width_shift_range = 0.2,

height_shift_range = 0.2,

horizontal_flip = True,

vertical_flip = True

)

​

and then flow from file to get the preprocessed images

I have tried that at first since it was intuitive and a benchmark since it's less parameters, but two layers gave better results, also the GAP has caused the training to early stop very early on, what do you suggest to as the top layer, eg GAP, batchnorm, dense

7 classes are equally distributed (500 images), only 1 has like 25% of the other data share (150-ish), it is a problem but I'm not sure how to solve it considering the fact that it's a challenge and I can't add data, augmentation will keep the imbalance since it augments everything equally.

Very insightful, Haven't tried most of these things, thanks for sharing the knowledge.

Will try that thanks

It's a challenge the test is online on unseen data, and I'm shuffling the split data each run

Yeah the problem is that this is a challenge and the data is limited, tried data augmentation but haven't had much luck.

However, I must ask, when using data augmentation is it better to augment the training and the validation sets or just the training?, seen conflicted opinions online.

Never worked with lightning, may sound dumb but, how does changing the library change the output of the learning process?

Yes that's the first step I do, after that step I try to unfreeze and fine tune

The library?

The DenseNet201(functional) layer is the full CNN but it's collapsed because it's>700 layer, will try those, thank you

I have been trying all of the Keras API transfer models but no luck, any suggestions on a newer model, I know the models will behave according to the problem but I'm ready to test anything rn, also any tips on the FC architecture?