Cat breeds classification website

The idea of this application is to classify the cat images that the user uploads and give the user the cat's breed.

I divided this project into 3 main stages:

The dataset.
Modeling.
Building the website.

The dataset

I first started by using this dataset on Kaggle But the dataset was really dirty, and there were a lot of problems with that dataset:

A lot of pictures were wrong, I even found bunny pictures instead of cat pictures in the Havana class folder!
The folders: "Tortoiseshell", "Tabby", "Torbie", "Dilute Tortoiseshell", "Calico", "Tuxedo", "Silver", "Domestic Short Hair", "Domestic Medium Hair" and "Domestic Long Hair" aren't really cat breeds, that kind of cats have a blended ancestry and that's why they could be really confusing to any model to recognize them, and the "Domestic Short Hair" folder got about 49,900 images alone - the dataset has about 118,000 images in total - so it created a highly imbalanced dataset.
There were a number of classes with very few images in them, some folders had only 4 images!

So I started building a better dataset.
I used 3 different cat breed datasets to construct this dataset along with Google images.
I used all the classes from the Gano Cat Breed Image Collection except for the "Tuxedo" which isn't technically a breed, that was 14 classes.
I used the Scottish fold class from the Cat Breed dataset.
The rest of the 27 classes came from the Cat Breeds Dataset (Cleared), those 27 classes contained a lot of bad images - misclassified and low quality - and I cleaned them as much as I could manually, also I collected more data for the Selkirk Rex, Korat, and Chartreux classes from Google images to make them at least 100 images.
After that I split the dataset into train and test, the test folders contain about 25% of the original dataset, and the train folders contain about 75% of the dataset.

You can find the new dataset on that repo: https://github.com/Aml-Hassan-Abd-El-hamid/datasets

Even after Building a new dataset dealing with this task was extremely hard for the following reasons:

The cat isn't always the only thing in the image or even the centre of it and the lighting and the quality vary from one image to anther one.
There's a huge variation inside the class, you can see for example those pictures that come from the "Abyssinian" class:

There are a lot of similar cat breeds that are really hard to differentiate from each other:

for example, the image on the left is from the "Siamese" class and the one on the right is from "The Burmese" class:

Another example, the image on the left is from the "Bengal" class and the one on the right is from the "Egyptian Mau" class:

Modling

I used Transfer learning to help with modelling this dataset, if that's your first time hearing of transfer learning I recommend those 2 tutorials as they were really useful:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
https://www.learnpytorch.io/06_pytorch_transfer_learning/

I experimented with a variety of pre-trained classes for this task.

I used AlexNet, Inception, ResNet18, ResNet34, and ResNet50.

The one who won this battle was ResNet50 which gave me 69% training accuracy and 66% test accuracy.

Some classes of curses got better accuracy than others, the class with the best accuracy -almost 100%- is the Sphynx class which makes sense because this breed of cat looks very unique.

Building the website

As someone who has never dealt with backend or machine learning deployment before this project, I wanted this project to be a gentle and nice start for me, I used Flask for the backend and Bootstrap 5 for the front end, I found a couple of good tutorials that I would like to share:
https://towardsdatascience.com/build-a-web-application-for-predicting-apple-leaf-diseases-using-pytorch-and-flask-413f9fa9276a
https://www.w3schools.com/bootstrap5/

Preview :

06.03.2023_09.16.24_REC.mp4

To do next:

1 - upgrade the performance:

Try to clean the data more. ✅
Gather more data from the classes with fewer images.✅
Try different models: a - Transformers? b - Try creating 2 different models: one to detect the cat in the image and the other to classify it.

2 - upgrade the website: