<!DOCTYPE html>
Image Classification with UI
Image classification is a popular task in the field of computer vision, where the goal is to categorize images into different classes or labels. In this article, we will discuss an end-to-end image classification example using PyTorch and Python. We will also create a user interface (UI) for easy interaction with our image classification model. Let’s get started!
PyTorch and Python
PyTorch is a popular deep learning framework developed by Facebook AI Research, which provides a flexible and easy-to-use platform for building and training neural networks. Python is a widely-used programming language for data science and machine learning, making it a perfect choice for implementing image classification models.
End-to-End Image Classification Example
For our image classification example, we will use a pre-trained deep learning model called ResNet-18, which is famous for its performance on image classification tasks. We will fine-tune this model on a dataset of images, and then use it to classify new images into different categories.
We will write Python code to load the pre-trained ResNet-18 model, fine-tune it on our dataset, and make predictions on new images. We will then create a user interface (UI) using HTML and JavaScript to allow users to upload images and see the classification results in real-time.
Creating the User Interface
Below is a simple HTML form that allows users to upload an image file and see the predicted class label:
document.getElementById(‘imageForm’).addEventListener(‘submit’, async function(event) {
event.preventDefault();
const formData = new FormData(this);
const response = await fetch(‘predict.py’, {
method: ‘POST’,
body: formData
});
const result = await response.json();
document.getElementById(‘results’).innerHTML = `Predicted class: ${result.class}`;
});
Conclusion
In this article, we discussed the concept of image classification and how to implement an end-to-end image classification example using PyTorch and Python. We also created a user interface (UI) for easy interaction with our image classification model. By following this example, you can build your own image classification system and deploy it with a user-friendly interface.
Sir , your doing wonderful project. But we all are beginner some kind zoom your code when see through phone is not visible. So kindly zoom your screen and take for beginners Level e