## Problem Domain
In this competition, you will work on a computer vision task, which involves training a model to recognize patterns in images. Specifically, the model needs to distinguish between two classes: plates that are empty (i.e., no food or objects on them) and plates that are not empty (i.e., plates with food or other items). This problem is part of a broader domain of image classification, where the goal is to teach a machine to understand visual content and make predictions based on the features present in an image.

## Key Challenge
The key challenge is data variability, as images can vary in lighting, angles, and the amount of food or objects on the plates. To build a robust model, you’ll need to:

- Preprocess and augment the images to improve model generalization.
- Train a classifier that can identify subtle features between empty and not-empty plates.
- Handle data imbalance, as the number of images in each class may not be perfectly equal.

## Problem Setup
Participants will have access to a labeled dataset of images of plates, where each image is tagged as either containing an empty plate or a not-empty plate. You'll use this dataset to train your models and make predictions.

## Key Requirements:

- Preprocessing steps such as resizing, normalization, or augmentation.
- Selection of the appropriate machine learning or deep learning model (e.g., CNNs).
- Fine-tuning of hyperparameters to improve accuracy.

## Objective

Build a model that predicts, for each image, whether the plate is empty or not. The model will be evaluated based on its accuracy, with more complex solutions and creative approaches receiving higher marks.