Fatiima-Ezzahra / Dog-Breed-Classification

In this project, weo build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Dog Breed Classification

Overview

In this project, weo build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Datasets

We used two datasets in this project :

• The dog dataset.
• The human dataset.

Key Steps

Step 1: Detect Humans

First, we used OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

OpenCV provides many pre-trained face detectors, stored as XML files on github. We chose the frontal face detector to used in this step.

Step 2: Detect Dogs

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)

Step 5: Write the Algorithm

Step 6: Test the Algorithm