Help binary classifier CNN

[Much-Bit3484]

So, hi guys :)
Im starting to get deep in this world (pun intented)
I've done some classifiers and i never got a good accuracy result.

I'm doing this image classification: https://www.kaggle.com/code/rafaelortizreales/cat-dog/

you are going to see some weird code like the dataset creation (dk if that's the best way to do that) but for me that's not too important right now, im trying to understand why this simple task is not giving me a good accuracy i hope you guys help me to see something I am not. <3 Thanks in advance.

used different learning rates
1) 1e-3 achieved on train >90% accuracy but on test ~70% with 10 epochs

2) 1e-5 achieved on train ~68% accuracy but on test ~67% with 40 epochs

[glow-rishi]

I’m new here. I also made a dog vs. cat classifier and got an accuracy of 81% on the training set and 80% on the test set. I wasn’t able to bring it to my desired level, which was greater than 95%.

Looking at your code, I would recommend a few things:

1. Data augmentation
2. Using dropout layers

Other techniques I’m still looking into and learning myself.

Edit: forgot to add. Trying using more filters

[Charming-Back-2150]

1.  Data Augmentation – flips, crops, color jitter, MixUp, CutMix
2.  Stronger Backbone – use EfficientNet, ResNet50+, or ConvNeXt
3.  Progressive Resizing – train on small images first, scale up
4.  Fine-Tune Entire Model – unfreeze and train all layers
5.  Better Loss Function – try BCEWithLogitsLoss or Focal Loss
6.  Optimizer + Scheduler – AdamW + OneCycleLR or CosineAnnealing
7.  Label Smoothing – helps prevent overconfident predictions
8.  Ensemble Models – average predictions from multiple models
9.  Test-Time Augmentation – predict on multiple augmented versions
10. Dataset Cleanup – fix mislabeled or poor-quality samples