Good and Bad Classification of Parotta

Description: The project titled "Good and Bad Classification of Parotta Using Realme P1 5G Mobile Camera" focuses on developing an image-based machine learning system capable of automatically classifying parotta samples into two categories: good quality parotta and bad quality parotta. The objective of this project is to assist in food quality assessment by using computer vision techniques to identify freshness, appearance, texture, and visible defects in parotta samples. This system can help food industries, restaurants, bakeries, and consumers maintain quality standards and reduce food wastage. The dataset used in this project consists of more than 1000 images, with over 500 images of good-quality parotta and 500 images of bad-quality parotta. The images were captured using the Realme P1 5G smartphone, which is equipped with a 50 MP AI rear camera capable of capturing high-resolution images with excellent detail. The high-quality camera allows clear visualization of important features such as color, texture, surface condition, and shape of the parotta. Dataset Composition: Good Samples (Fresh and High-Quality Parotta): More than 500 images represent good-quality parotta. These samples exhibit desirable characteristics such as an even golden-brown color, soft texture, proper layering, uniform shape, and absence of burns, cracks, mold, or contamination. These images form the positive class and represent freshly prepared parotta suitable for consumption. Bad Samples (Poor-Quality or Spoiled Parotta): The dataset also contains more than 500 images of bad-quality parotta. These samples may show signs of spoilage, staleness, excessive dryness, hard texture, burnt surfaces, discoloration, fungal growth, contamination, or improper preparation. These images form the negative class and help the model learn to identify defective or unsafe parotta samples. Data Collection Setup: Images were captured using the Realme P1 5G mobile camera under controlled conditions. A black background was used to create strong contrast between the parotta and the surroundings, enabling easier feature extraction. The photographs were taken under natural daylight conditions supplemented with 360-degree LED lighting, ensuring uniform illumination and reducing shadows or lighting variations. This setup improved image consistency and enhanced the visibility of surface features. Image Characteristics: The dataset includes parotta samples with variations in size, thickness, shape, texture, and preparation methods. Such diversity is important for training a robust machine learning model capable of accurately classifying different types of parotta under real-world conditions. The images capture a wide range of quality levels, making the dataset suitable for practical food quality inspection applications. Data Annotation: Each image was manually labeled as either "Good" or "Bad" based on visual quality assessment and expert observation.本项目题为「基于Realme P1 5G智能手机摄像头的帕罗塔（Parotta）优劣分类」，旨在开发一套基于图像的机器学习系统，可自动将帕罗塔样本划分为优质帕罗塔与劣质帕罗塔两类。本项目的目标是借助计算机视觉（Computer Vision）技术，识别帕罗塔样本的新鲜度、外观、质地及可见缺陷，以此助力食品质量评估工作。该系统可协助食品企业、餐厅、面包坊及消费者维持质量标准，减少食品浪费。 本项目所使用的数据集总计包含超1000张图像，其中优质帕罗塔图像逾500张，劣质帕罗塔图像约500张。所有图像均通过搭载5000万像素AI后置摄像头的Realme P1 5G智能手机拍摄，该摄像头可捕捉细节表现力优异的高分辨率图像，能够清晰呈现帕罗塔的色彩、质地、表面状态及形状等关键特征。 数据集构成： 优质样本（新鲜优质帕罗塔）： 共计逾500张优质帕罗塔图像。此类样本具备理想的外观属性：色泽均匀呈金棕色、质地柔软、层次分明、形状规整，且无焦糊、裂纹、霉变或污染等问题。该类图像作为正样本，代表可供安全食用的现制帕罗塔。 劣质样本（劣质或已变质帕罗塔）： 数据集同时包含逾500张劣质帕罗塔图像。此类样本可表现出变质、失鲜、过度干燥、质地坚硬、表面焦糊、色泽异常、真菌滋生、受污染或制作不当等问题。该类图像作为负样本，可辅助模型学习识别存在缺陷或不安全的帕罗塔样本。 数据采集设置： 图像采集于可控环境中，采用Realme P1 5G智能手机摄像头完成拍摄。采集过程使用黑色背景以强化帕罗塔与背景的对比度，便于后续特征提取。拍摄时以自然光为主，辅以360度LED补光，确保光照均匀，减少阴影与光照差异。该设置提升了图像一致性，同时增强了表面特征的可视性。 图像特征： 本数据集涵盖尺寸、厚度、形状、质地及制作工艺各异的帕罗塔样本。此类样本多样性对于训练鲁棒性优异的机器学习模型具有重要意义，可使模型在真实场景下准确分类各类帕罗塔。数据集覆盖了广泛的质量等级范围，因此适用于实际的食品质量检测应用。 数据标注： 每张图像均基于视觉质量评估与专家观察，被手动标注为"Good"或"Bad"。