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.

数据集描述：优质与劣质煎蛋卷（Omelette）分类
本数据集专为煎蛋卷样本优劣识别的二分类任务构建，核心目标是开发可基于预设质量参数，精准完成煎蛋卷图像或样本分类的机器学习模型。

1. 数据集规模与结构
本数据集共包含2000条样本，分为两类且数量均等：
1000条优质煎蛋卷样本
1000条劣质煎蛋卷样本

数据集中每条样本对应一份煎蛋卷，并配有相应标签：以`1`或"Good"表示优质煎蛋卷，以`0`或"Bad"表示劣质煎蛋卷。

2. 数据类型
数据集可包含以下内容：
图像：光照条件统一的高分辨率煎蛋卷实拍照片，每张图像均配有对应标签。
可选元数据（如已提供）：
纹理指标（如酥脆度、蓬松度）、色彩平衡度（金棕色 vs 焦糊或未熟透）、形状规整度、所用食材、烹饪时长与温度。

3. 质量判定标准（标注规范）
(i) 优质煎蛋卷特征：
烹饪均匀（无焦糊或未熟透情况）、外观诱人的金棕色泽、质地均衡（无过韧或过度酥脆问题）、造型规整且视觉效果美观、包含标准配料（如鸡蛋、牛奶、调味料，可选搭配蔬菜）。

(ii) 劣质煎蛋卷特征：
未熟透或过度烹饪（焦糊）、色泽过浅或过深、形状不规则、破损或折叠不当、质地不佳（如过于稀软或过韧）、配料缺失或配比错误。

4. 数据集用途
本数据集可用于：
训练与评估计算机视觉或质量评估模型、食品质量管控中的图像分类任务、对比不同机器学习算法在二分类任务中的性能表现。

5. 应用场景
(i) 餐厅或外卖服务中的自动化食品质量检测；
(ii) 厨艺培训的教学辅助工具；
(iii) 预包装餐食生产中的质量保障环节。

6. 图片上传要求
需上传1000张符合规范的优质煎蛋卷图片与1000张劣质煎蛋卷图片。

7. 伦理与偏倚考量
本数据集在样本采集阶段已充分兼顾多样性，涵盖不同烹饪手法、光照条件与摆盘方式，以规避偏倚问题。