Good and bad Classification and identification of Omelette

Dataset Description: Good and Bad Omelette Classification

This dataset is created for the binary classification task of identifying whether an omelette sample is of good or bad quality. The primary objective is to develop a machine learning model that can accurately classify an omelette image or sample based on predefined quality parameters.

1. Dataset Size and Structure:
The dataset consists of 2000 samples, equally divided into two classes:

1000 Good Omelettes

1000 Bad Omelettes

Each sample in the dataset represents one omelette and is accompanied by a corresponding label:

1 or "Good" for high-quality omelettes

0 or "Bad" for low-quality omelettes

2. Data Type:
The dataset may include:

Images: High-resolution photos of omelettes under consistent lighting conditions. Each image is labeled accordingly.

Optional Metadata (if available):

Texture metrics (e.g., crispiness, fluffiness)

Color balance (golden brown vs burnt or undercooked)

Shape regularity

Ingredients used

Cooking time and temperature

3. Quality Criteria (Labeling Guidelines):

i) Good Omelette Characteristics:

Evenly cooked (not burnt or undercooked)

Appealing golden-brown color

Balanced texture (not rubbery or overly crispy)

Well-shaped and visually appealing

Includes expected ingredients (e.g., eggs, milk, seasoning, optional vegetables)

ii) Bad Omelette Characteristics:

Undercooked or overcooked (burnt)

Pale or overly dark in color

Irregular shape, torn or folded poorly

Displeasing texture (e.g., too runny or rubbery)

Missing or wrong ingredients

4. Purpose of the Dataset:

The dataset is intended for:

Training and evaluating computer vision or quality assessment models

Image classification tasks in food quality control

Benchmarking performance of different ML algorithms in binary classification

5. Applications:

i) Automated food quality inspection in restaurants or food delivery services

ii) Educational tools for culinary training

iii) Quality assurance in pre-packaged meal production

6. upload pictures:
upload proper bad 1000 pictures and good 1000 pictures.

7. Ethics and Bias Consideration:

Care has been taken to ensure diversity in sample acquisition—different cooking styles, lighting, and plating are considered to avoid bias.