Food Classification using Deep Learning

This project focuses on using deep learning techniques to classify food items from images. The goal is to develop models that can accurately identify different types of food, aiding in the categorization process for an online food delivery application.

Dataset

The dataset consists of two main parts: a training set and a testing set. The training set contains images categorized into 22 classes, each representing a different type of food item. The testing set comprises 2200 unlabeled images, which will be used for final evaluation.

Approach

1. Data Preparation: Download and preprocess the dataset, splitting it into training and validation sets.

2. Model Selection: Experiment with various deep learning architectures such as AlexNet, ResNet, VGG, Inception, MobileNet, etc., or even custom models.

3. Training: Train the selected model using the training data. Fine-tune hyperparameters like learning rate, network size, and number of epochs to achieve optimal performance.

4. Evaluation: Assess the model's performance using the validation set. Tune the model further if necessary.

5. Prediction: Utilize the trained model to predict labels for the testing dataset.

6. Submission: Prepare a CSV file containing the predicted labels for the testing dataset and submit it for final evaluation.