Have you been looking for ways to create image recognition software for a long time? If so, that is what this article will discuss.
Introduction
Machines are using their senses to get things done. Such as pattern recognizing, planning, and understanding of natural language. As well as learning and solving some problems. And one of the machine senses is image recognition.
Moreover, image recognition has risen as a useful tool. Also for some upcoming inventions, it is important.
So, why don’t you create your own image recognition software? And that too in some lines of code. Machine learning libraries today are a big help. Then let’s start.
How To Create Image Recognition Software?
Now, let’s make a simple image recognition tool first. A tool that analyzes whether the picture is a dog or a cat.
Tools And Technology
- Anaconda
Free and open distribution of R and Python programming languages. This is for applications related to data science and machines. Also, it goals to make simpler deployment and package management.
- Spyder
This tool is an open-source cross-platform IDE. Also, for the Python language science program. It is installed with anaconda. But if not, install it using the anaconda navigator.
- Tensorflow
This tool is an open-source software library. Usually for dataflow programming in various tasks.
- Keras
Keras is still open source, but in a neural network library that was written in Python. Enable Tensorflow env. Also, installing Keras using ‘pip install Keras’ is needed.
- CNN
This one is a class of feed-forward, deep artificial neural networks. Most generally applied in the study of visual imagery.
Plan of Attack
So now, it’s time to act. Following is the plan:
- Step 1 – Collecting Dataset
To train our machine, we need a large amount of data. With the goal that our model can gain or learn from them. By defining some correlations with the objects. As well as common features associated with the objects.
- Step 2 – Importing Libraries and Dataset Sharing
In order to use the powers of libraries, we must first import them. After that, we need to divide our data into two parts. Such as the training set containing 4000 pictures. While the test_set contains 1000 pictures.
- Step 3 – Developing CNN
Our networks’ most essential step. But it consists of the following three parts:
- Convolution – it’s extracting features from the input picture.
- Polling – decreases the dimensionality of each component map. But retains the important information.
- Flattening – where the matrix is changed to a linear array. So that includes it into the hubs of our neural network.
- Step 4 – Full Connection
This connection connects our convolutional organization to a neural network. Afterward, our network accumulates.
- Step 5 – Data Enhancement
One way we can reduce over-fit models. So we increase the volume of training data that only uses information in our training data.
- Step 6 – Training your Network
Moreover, training can be done in less than an hour. But with a good video card. As well as having enough RAM.
- Test It
You can test it now and see what it predicts.
