Kuler achar

*Project Title:* Good and Bad Classification of Kuler Achar Using OnePlus Nord CE 2 Lite 5G Mobile Camera

*Description:*

The project "Good and Bad Classification of Kuler Achar" is focused on developing a system for automatically classifying kuler achar (Indian jujube pickle) into "good" and "bad" categories based on visual characteristics. The dataset includes over 500 images of kuler achar, equally divided between good and bad samples. These images were captured using the OnePlus Nord CE 2 Lite 5G mobile camera, ensuring high-quality visual data for training a machine learning model. The images were taken against a white background in daylight conditions to ensure consistency, clarity, and minimal environmental interference.

*Dataset Composition:*

- *Good Samples (High-Quality Kuler Achar):* The dataset contains more than 250 images of high-quality kuler achar. These samples exhibit desirable characteristics, such as consistent texture, proper fermentation, vibrant color, and no visible signs of contamination or spoilage. These images represent the positive class for the classification model, showcasing what is considered high-quality and safe kuler achar.

- *Bad Samples (Low-Quality Kuler Achar):* More than 250 images depict low-quality or spoiled kuler achar. These samples may exhibit visual signs of spoilage, such as mold growth, discoloration, uneven texture, or over-fermentation. These images form the negative class for model training, helping to identify unwanted and unsafe products.

*Data Collection Setup:*

All images were captured using the OnePlus Nord CE 2 Lite 5G mobile camera, which provides high-resolution imaging capabilities to capture fine details. A white background was chosen to create a neutral, non-distracting environment that highlights the key features of the kuler achar. Daylight conditions were used for consistent and natural lighting, allowing the camera to capture the true color, texture, and visual quality of the samples.

*Image Characteristics:*

The dataset features a wide range of kuler achar samples, varying in appearance due to different stages of fermentation, levels of spoilage, and types of ingredients. This diversity ensures that the classification model can generalize across different variations in the appearance of kuler achar, allowing it to effectively distinguish between good and bad samples.

*Data Annotation:*

Each image in the dataset is carefully labeled as either "good" or "bad" based on its quality. These annotations serve as the ground truth for the machine learning model, providing the necessary data for training, validating, and testing the classifier.

*Data Preprocessing:*

To ensure the model receives optimal input data, several preprocessing steps are applied:
- *Resizing:* All images are resized to a uniform resolution to maintain consistency across the dataset.

*项目名称：基于OnePlus Nord CE 2 Lite 5G移动摄像头的印度枣泡菜（Kuler Achar）优劣分类*

*项目描述：*
本项目“印度枣泡菜（Kuler Achar）优劣分类”旨在开发一套基于视觉特征的自动分类系统，将印度枣泡菜划分为“优质”与“劣质”两类。本数据集包含超500张印度枣泡菜图像，优质与劣质样本数量均等。所有图像均采用OnePlus Nord CE 2 Lite 5G移动摄像头拍摄，以获取高质量视觉数据用于机器学习模型训练。拍摄场景统一设置为白色背景与日间自然光环境，以确保数据一致性、清晰度，并最大限度减少环境干扰。

*数据集构成：*
- *优质样本（高品质印度枣泡菜）：* 数据集包含超过250张高品质印度枣泡菜图像。此类样本具备理想品质特征，如质地均匀、发酵程度适宜、色泽鲜亮，且无可见污染或变质迹象。这些图像作为分类模型的正样本，代表了符合高品质安全标准的印度枣泡菜。
- *劣质样本（低品质印度枣泡菜）：* 包含超过250张低品质或已变质的印度枣泡菜图像。此类样本可观察到变质视觉特征，如霉菌滋生、色泽异常、质地不均或过度发酵。这些图像作为模型训练的负样本，用于识别不合格且不安全的产品。

*数据采集设置：*
所有图像均采用OnePlus Nord CE 2 Lite 5G移动摄像头拍摄，该设备具备高分辨率成像能力，可捕捉细微细节。采集时选用白色背景以营造中性、无干扰的环境，突出印度枣泡菜的核心特征。拍摄采用日间自然光以保证光照一致性与自然度，使摄像头能够准确还原样本的真实色泽、质地与视觉品质。

*图像特征：*
本数据集涵盖了多样化的印度枣泡菜样本，因发酵阶段、变质程度与配料类型的不同而呈现外观差异。这种多样性可确保分类模型能够适配印度枣泡菜的各类外观变化，有效区分优质与劣质样本。

*数据标注：*
数据集中的每张图像均基于其品质被严格标注为“优质”或“劣质”。这些标注作为机器学习模型的真实标签，为分类器的训练、验证与测试提供必要的数据支撑。

*数据预处理：*
为确保模型获得最优输入数据，本数据集采用了以下预处理步骤：
- *尺寸统一化：* 将所有图像调整至统一分辨率，以保证数据集内的一致性。