Project Title: Cat vs Dog Image Classifier
This project aims to classify the input image as either a dog or a cat image. The image input which you give to the system will be analyzed and the predicted result will be given as output. Machine learning algorithm [Convolutional Neural Networks] is used to classify the image.
The model thus implemented can be extended to a mobile device or any website as per the developer’s need.
You can get the complete code for the project here .
The project is entirely implemented using Python3. The Conceptual Framework involved is mainly:
To understand the flow of work, please see the attached image below or click here .
Step 1 : Getting the Dataset
The dataset is available here .
Step 2: Installing Required Packages [Python 3.6]
1. OpenCV —> ‘3.4.0’ [ Used to handle image operations like reading the image, resizing, reshaping]
2. numpy —> ‘1.14.4’ [ Image that is read will be stored in an numpy array ]
3. TensorFlow —> ‘1.8.0’ [ Tensorflow is the backend for Keras ]
4. Keras —> ‘2.1.6’ [ Keras is used to implement the CNN ]
Step 3: How the Model Works ??
The dataset contains a lot of images of cats and dogs. Our aim is to make the model learn the distinguishing features between the cat and dog. Once the model has learned, i.e once the model got trained, it will be able to classify the input image as either cat or a dog.
Data structures and Algorithms used in project
Github Link: https://github.com/Yatheen07/cat-dog-image-classifier
This project gives a general idea of how image classification can be done efficiently. The scope of the project can be extended to the various industries where there is a huge scope for automation, by just altering the dataset which is relevant to the problem.
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