Tensorflow implementation to classify Cats and Dogs. Please don't ask me why I did this.
Dataset used can be found HERE.
Make sure you have Python 3.8 and pip installed. The project contains two requirements files, being one for ROCM and other for Apple M1 silicon.
python3 -m venv ./env
source ./env/bin/activate
pip install -r <requirements-file.txt>
usage: run.py [-h] [-t TRAIN] [--nosave] [--vgg16] [-p PREDICT] [-pa PREDICT_ALL] [--check_images CHECK_IMAGES] [--debug]
optional arguments:
-h, --help show this help message and exit
-t TRAIN, --train TRAIN
Train the model using N epochs.
--nosave Set no_save flag. Trained models won't be saved.
--vgg16 Use the Keras VGG16 model.
-p PREDICT, --predict PREDICT
Predict an image class. -p <IMG_PATH>
-pa PREDICT_ALL, --predict_all PREDICT_ALL
Predict all images inside a folder. -pa <FODLER_PATH>
--check_images CHECK_IMAGES
Check if images in specified folder are not corrupted.
--debug Change log level to DEBUG.
Just run run.py -t <n_epochs> to train the model running all the images on the dataset, where <n_epochs> is the the number of times the software will go through the dataset.
You can use run.py -p <img_path> to predict a single image, trying to tell if it is a cat or a dog. If you have a folder with some photos of cats and dogs, you can use run.py -pa <folder_path> and the software will try to predict all the images inside this folder.
A web version of this project can be found HERE.
I used Midjourney to generate the background image, and ChatGPT to generate the classification response phrases.
The software contains two CNN Sequential Model implementations. One is a custom Model build, based on the Keras VGG16 implementation, and the other is the original Keras VGG16 Model.
To use the original Keras VGG16 Model, just use the --vgg16 argument when running run.py.