Image Classification Web Application

Project Domain / Category

Web Development / Artificial Intelligence

Introduction:

We aim to utilize Python and the Django web framework to develop an intelligent image classification application. By leveraging pre-trained deep learning models, we can automate the categorization of user-uploaded images into predefined classes such as everyday objects, animals, and vehicles. This demonstrates the practical integration of AI into accessible web platforms, allowing users to benefit from state-of-the-art computer vision capabilities through a simple interface.

Functional Requirements for Web Application:

            Getting User Input:

Develop a user interface where users can upload or input an image file through a web browser.

            Classify the Image:

Use a deployed pre-trained deep learning model to predict the content of the uploaded image. Preprocess the uploaded image to ensure it matches the input format expected by the model

(resizing, normalization).

Pass the pre-processed image through the deployed model for inference.

The following steps are to be followed carefully:

      Image Pre-processing:

        Resize images to the required dimensions and normalize pixel values

        Apply necessary colour channel conversions and array transformations

        Model Selection:

        Utilize pre-trained models such as ResNet, VGG16, or EfficientNet with transfer learning

        Select models based on optimal balance between accuracy and inference speed

          Feature Extraction:

        Leverage the model's convolutional layers for automatic feature extraction

        Utilize deep learning architectures specifically designed for image recognition tasks

          Performance Evaluation:

        Evaluate model performance using appropriate metrics for multi-class classification

        Validate model predictions against benchmark datasets

        Model Implementation:

        Implement the model using TensorFlow/Keras integrated with Django backend

        Ensure efficient model loading and inference pipeline

          Accuracy Assessment:

        Verify model accuracy using standardized test datasets

        Monitor prediction confidence scores for reliability assessment

            Show the Class Name:

Once the model predicts the image content, display the corresponding classification label (e.g.,

"Golden Retriever", "Sports Car", "Coffee Mug").

            Show the Confidence Score:

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