Good and bad classification of Egg poach

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Project Title: Good and Bad Classification of Egg Poch

Project Description:

The aim of this project is to develop a reliable classification system that can differentiate between good and bad "egg pochs" based on image data. An "egg poch" refers to a poached egg, and the quality assessment will be made based on visual features. This classification is useful for automating quality control in food production environments, particularly in commercial kitchens or food manufacturing settings where consistency and quality are crucial.

Data Description:

The dataset consists of labeled images of poached eggs that fall into two categories:

Good Egg Poch – These are poached eggs that meet standard quality criteria such as intact yolk, proper shape, clean whites, and no over- or under-cooking.

Bad Egg Poch – These include poached eggs that are visually unappealing due to broken yolks, excessive spreading, undercooked or overcooked whites, or any deformities.

Key Features of the Dataset:

Image Format: JPEG/PNG format, standardized to a fixed resolution (e.g., 224x224 or 256x256 pixels) for uniform input.

Labels: Binary labels — "Good" or "Bad."

Image Quality: All images are clear and taken under controlled lighting and angle conditions to minimize noise.

Sample Size: The dataset contains a balanced number of images from both classes to avoid bias in classification.

Augmentation: Data augmentation techniques such as rotation, flipping, brightness adjustment, and zoom have been applied to increase diversity and improve generalization of the model.

Data Collection Method:
Images were collected using high-resolution cameras in a controlled kitchen environment. Each egg poch was photographed immediately after preparation. Food experts manually annotated each image based on predefined visual standards to ensure label accuracy.

Use Cases:

Automated quality assurance systems in food processing units.

Kitchen assistant applications that provide feedback on egg preparation.

Visual training datasets for AI culinary robots or educational tools.

Challenges:

Subtle visual differences between good and slightly bad eggs.

Potential subjectivity in manual labeling.

Variations in lighting and camera angle if not controlled properly.

Ethical Considerations:

No sensitive or personally identifiable information is contained in the dataset.

All food items were handled and disposed of in accordance with food safety regulations.