Dog Breeds Classification With CNN Transfer Learning

Dog Breeds Classification With CNN Transfer Learning

Dog Breeds Classification With CNN Transfer Learning: Transfer learning make use of the knowledge gained while solving one problem and applying it to a different but related problem.

Transfer learning make use of the knowledge gained while solving one problem and applying it to a different but related problem. They utilize knowledge acquired for one task to solve related ones.

The project is divided into seven steps and I am going to walk you through each of these steps.

1. Importing the Datasets

Dog Data Set

The dog data set is divided and loaded into train, validation and test groups. There are a total of 8351 images each belonging to one of the 133 dog breeds. The division for the train, validation and test set are made in the ratio of 80:10:10 .

Human Data Set

There are a total of 13233 human images loaded into an array.

2. Detecting Humans

In this step we make use of the Haar feature-based cascade classifiers for detecting the human face in the images. We make use of a pre-trained model from OpenCV, haarcascades for the face detection.

Image for post

A sample output of the face detection model used in this project

We then assess this face detector by passing in images of human and dogs. The model is able to detect human faces with a probability of 100% however when we pass an image of the dog the model detects the face only 11% of time.

data-science data analysis

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