Image classifier for identifying cat vs dogs using CNN and python

We will make our very own Image Classifier which can recognize whether a given pic is a cat or dog or something different relying on your sustained info. To accomplish our objective, we will utilize one among the acclaimed AI calculations out there that are utilized for Image Classification, for instance, Convolutional Neural Network (or CNN). 

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Project Description

CNN (Convolutional Neural Network)

CNN is a class of profound neural systems, most ordinarily applied to break down visual imagery.ep neural systems, most regularly applied to investigate visual symbolism.

Build CNN

This progression is one among the foremost vital strides of the task and It contains 3 sections

• Convolution- The essential role of Convolution is to extricate highlights from the image. Convolution saves the spatial connection between pixels by learning image highlights utilizing little squares of information.
• Pooling-Pooling is also known as subsampling or downsampling and it decreases the dimensionality of each element map yet holds the most significant data.
• Flattening - The matrix changed over into a linear array that to enter it into the hubs of our neural system.

Machine Learning

Ml is characterized because the field of study that offers computers the potential to find out while not being expressly programmed and furthermore it's only one method for showing the machine by nourishing large amounts of data. Here within the project, we need an outsized quantity of dataset of cats and dogs for image classification.

Project Implementation

We have to play out some essential strides to accomplish our objectives and steps are:

1. At first, we have to gather the tremendous amount dataset from the web to prepare the machine, So, that our model can gain from them and perform classification.
2. After downloading the dataset, we need to split the dataset into 2 parts i.e. training_dataset and test_dataset.
3. The next step is to apply some CNN concepts and build CNN.
4. The next step is to connect the whole neural network i.e. connecting our convolutional network to a neural network and afterward assembling our network.
5. While training our data, we will need a lot of data to train upon. So, to get more data, we simply need to make minor changes to our current dataset and this procedure is known as Data Augmentation, Data augmentation is a way we can lessen overfitting on models, where we increment the measure of preparing information utilizing the information only in our training data.
6. Now we need to train our model.
7. Lastly, our model is prepared, now we need to test the model.
8. Here we go our model is ready for image classification.

Software Requirement 

Programming language - Python, Keras, TensorFlow

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Image classifier for identifying cat vs dogs using CNN and python