Training ImageNet- VGG16

I am trying to train(kind of finetuning) the VGG16 network.
I am using a custom set of CNN filters and am trying to retrain the final dense classification layer only. I am using the default settings provided in

Where I have modified model creation from lines 73-88, to import the model with my CNN filters. I have also made sure that “requires_grad=True” parameter is enabled only for the dense layer.

This is my result after around 22 epochs,
Epoch: [22][4510/5005] Time 1.438 (2.538) Data 0.001 (1.757) Loss 6.9070 (6.9070) Prec@1 0.000 (0.103) Prec@5 0.391 (0.520)

As you can see prec@1 is Zero!

I suspect there is something wrong in the data loading and labeling of the imagenet dataset.
I downloaded and extracted the ILSVRC2012 file to obtain train,test and eval.

The folder names inside train/ directory corresponds to some kind of synset id (“n02084071”).
I am assuming the pytorch loaders use this as the class label.

The eval/ folder does not come with the same set of folders, instead comes with a .txt file that maps the images to numerical classes (1-1000). So I wrote a script to create folders accordingly for each images using the numerical class labels, using the data/metadata.mat file (this file has all the info & minute details)

Why is my precision zero, even after 22 epochs?
Please do inform if my data processing methods are wrong or feel free to point out any other errors.
Please help !

Hi @abhyantrika. Have you been able to solve the issue? I have the same problem. My guess is that the pretrained model uses labels in different orders than the one you have in the train/val datasets.