TO RUN CLASSIFIER PLEASE USE THIS COLAB NOTEBOOK:

• https://github.com/yarusx/cat-vs-dogo/blob/main/dog_cat_breed_0_0_2.ipynb

• You could look close at this use case on my YouTube channel: https://youtu.be/JKebMRNHfGo

Dogs breeds:

• 'pug', 'basset_hound', 'shiba_inu', 'leonberger', 'saint_bernard', 'miniature_pinscher', 'wheaten_terrier', 'scottish_terrier', 'pomeranian', 'german_shorthaired', 'english_setter', 'newfoundland', 'boxer', 'great_pyrenees', 'samoyed', 'japanese_chin', 'english_cocker_spaniel', 'havanese', 'american_pit_bull_terrier', 'keeshond', 'american_bulldog', 'chihuahua', 'beagle', 'yorkshire_terrier', 'staffordshire_bull_terrier'

Cats breeds:

• 'egyptian_mau', 'siamese', 'birman', 'abyssinian', 'sphynx', 'british_shorthair', 'bombay', 'russian_blue', 'persian', 'ragdoll', 'maine_coon', 'bengal'

At this moment Project has such steps:

• build dog vs cat model (https://github.com/yarusx/cat-vs-dogo/blob/main/cat_vs_dog_0_0_2.ipynb).
• build dog model (https://github.com/yarusx/cat-vs-dogo/blob/main/dogs_breeds_0_0_2.ipynb).
• build cat model (https://github.com/yarusx/cat-vs-dogo/blob/main/cats_breeds_0_0_2.ipynb).
• predict dog vs cat => dog breed or cat breed with a pretty decent accuracy (https://github.com/yarusx/cat-vs-dogo/blob/main/dog_cat_breed_0_0_2.ipynb).

Training of all models has been produced with 'The Oxford-IIIT Pet Dataset': http://www.robots.ox.ac.uk/~vgg/data/pets/.

The Project is at the earliest stage of its production. Current validation accuracy: for a binary classification ~ 98.5%, for a cat breeds classification ~ 87%, for a dog breeds classification ~ 90%. So a lot of things should be and will be updated.