Classifying Cats vs Dogs with a Convolutional Neural Network

Description ✨

In that project, I ran through taking raw images that had been labeled for me already and then fed them through a convolutional neural network for classification. The images were either of dog(s) or cat(s).

Part 1 - Preprocessing

I had the data, but I couldn't exactly just stuff raw images right through my convolutional neural network. First, I needed all of the images to be the same size, and then I also probably wanted to just grayscale them. Also, the labels of "cat" and "dog" were not useful; I wanted them to be one-hot arrays. TensorFlow Fold made it easy to implement deep-learning models that operated over data of varying size and structure.

Part 2 - The Model

This Model differentiates whether an image is of a cat or dog using Convolutional Neural Networks.

Part 3 – Processing

1/Importing Libraries
2/Loading the Data
3/Building the Model
4/Compiling and Fitting the Model
5/Plotting Accuracy and Loss
6/Model Predictions

How to run locally 💻

Follow these steps to run catsVsDogs in your local machine.

Clone this repository

git clone https://github.com/Moanesbbr/catsVSdogs

Follow this video

Now, you can follow my presentation video here : https://www.youtube.com/watch?v=zzQjJSpIzbI&ab .