Merge pull request UppuluriKalyani#355 from mehul-m-prajapati/food_cl…

…assify

Added food classification

Computer Vision/Food Classification/README.md

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+Food Image Classification with TensorFlow
+=========================================
+
+This project demonstrates food image classification using TensorFlow and a pre-trained MobileNetV2 model. Users can upload an image of food and the model classifies it as either "pizza" or "burger."
+
+Table of Contents
+-----------------
+
+*   [Installation](#installation)
+*   [Usage](#usage)
+*   [Code Breakdown](#code-breakdown)
+*   [Possible Enhancements](#possible-enhancements)
+*   [Example Use Case](#example-use-case)
+*   [Requirements](#requirements)
+
+Installation
+------------
+
+To install the necessary libraries, run the following command:
+
+    !pip install tensorflow
+
+Usage
+-----
+
+1.  Upload an image of food (either pizza or burger) using the provided file upload feature in Google Colab.
+2.  The model will preprocess the image and make a prediction.
+3.  The predicted class and confidence score will be displayed.
+
+Code Breakdown
+--------------
+
+1.  **Library Installation**: Installs TensorFlow, NumPy, and Matplotlib.
+2.  **Loading Pre-trained Model**:
+    *   Loads the MobileNetV2 model pre-trained on the ImageNet dataset.
+    *   Freezes the base model's weights.
+3.  **Model Creation**:
+    *   Builds a custom model with a global average pooling layer and a dense output layer using sigmoid activation for binary classification.
+4.  **Model Compilation**: Compiles the model with the Adam optimizer and binary cross-entropy loss.
+5.  **Image Upload**: Allows users to upload images for classification.
+6.  **Image Preprocessing**: Resizes the image, converts it to an array, and preprocesses it.
+7.  **Making Predictions**: Classifies the uploaded image and outputs the predicted class and confidence score.
+
+Possible Enhancements
+---------------------
+
+*   **Multi-Class Classification**: Extend to classify more food items.
+*   **Data Augmentation**: Implement techniques to improve model accuracy.
+*   **Web Interface**: Create a user-friendly web application for easy image uploads.
+
+Example Use Case
+----------------
+
+This food image classification tool can be used for food identification, dietary tracking, and restaurant menu automation, allowing users to quickly classify and log their meals.
+
+Requirements
+------------
+
+*   TensorFlow
+*   NumPy
+*   Matplotlib
+
+### How to Run
+
+1.  Install the required libraries.
+2.  Upload an image of food.
+3.  Run the code to see the classification result and confidence score.