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https://github.com/aml-hassan-abd-el-hamid/cat-breeds-classification-website

cat breed classifer using Pytorch and flask
https://github.com/aml-hassan-abd-el-hamid/cat-breeds-classification-website

deep-learning deployment flask pytorch transfer-learning

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cat breed classifer using Pytorch and flask

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# 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 : 



https://user-images.githubusercontent.com/66205928/223043414-f518582c-b21b-47f0-a500-3c9c03e67ae4.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:

- Add a database to provide info about the cat in interest