seshansaravanan / food_vision_101

Food Vision 101 Classifier Project

This project revolves around building a deep learning model for food recognition using the Food Vision 101 dataset. The primary objective is to create a model capable of accurately categorizing a wide range of food items into 101 different classes. This project leverages various Python-based machine learning and deep learning libraries to analyze the dataset, develop predictive models, and evaluate their performance.

Table of Contents

• Problem Definition
• Dataset
• Evaluation
• Features
• Tools Used
• Data Exploration
• Modeling
• Model Evaluation
• Hyperparameter Tuning
• Conclusion

Problem Definition

In a nutshell, the problem can be defined as follows:

Given a vast dataset comprising images of various food items categorized into 101 classes, can we create a deep learning model capable of accurately recognizing and classifying these food items?

Dataset

The core dataset for this project is the Food Vision 101 dataset, which encompasses a whopping 101,000 food images. These images are divided into 101 distinct food categories, each containing exactly 1,000 images. This dataset serves as a fundamental resource for training, evaluating, and pushing the boundaries of food-related image recognition tasks.

Link to the Food Vision 101 dataset

Evaluation

The primary evaluation metric for this project is classification accuracy. The objective is to maximize the accuracy of our food recognition model in correctly classifying food images into their respective categories.

Features

The dataset is rich with features, including:

• Image: A collection of food images representing various dishes.
• Category: A label indicating the specific food category that each image belongs to.

Refer to the dataset's official documentation for a comprehensive list of food categories and additional details.

Tools Used

This project utilizes a wide range of Python libraries and tools to achieve its goals:

• TensorFlow and Keras: For building and training deep learning models.
• Matplotlib and Seaborn: For data visualization and model performance analysis.
• Scikit-Learn: For model evaluation and hyperparameter tuning.
• Jupyter Notebook: The primary development environment for the project.

Data Exploration

Exploratory data analysis (EDA) is a crucial step in this project. It involves examining data statistics, visualizing images and categories, and gaining insights into the distribution of food images to understand the dataset better.

Modeling

Various deep learning architectures will be explored to build the food recognition model, including:

1. Convolutional Neural Networks (CNNs)
2. Transfer Learning with Pretrained Models (e.g., VGG16, ResNet)

These models will be trained on the dataset to create a robust food recognition system.

Model Evaluation

The accuracy and performance of each model will be meticulously evaluated using validation data. Model comparisons will be conducted to select the most effective architecture.

Hyperparameter Tuning

Fine-tuning of hyperparameters will be performed to optimize model performance. Adjustments to learning rates, batch sizes, and network architectures may be explored to enhance food recognition accuracy.

Conclusion

The Food Vision 101 Classifier Project aims to create a deep learning model for food recognition using the Food Vision 101 dataset. The project spans data exploration, model development, evaluation, and fine-tuning. The ultimate goal is to build a highly accurate food recognition system that can identify and classify various food items into 101 distinct categories. Further enhancements and refinements of models are expected to achieve this goal.