Good and Bad Classification of pan fried momo
Description
The project titled “Pan Fried Momo Classification Using Moto G54 5G Mobile Camera” focuses on developing a machine learning and computer vision system to automatically classify pan-fried momo samples into two categories: good (fresh and properly cooked) and bad (stale, spoiled, burnt, or poor-quality). The objective of this project is to build an accurate image classification model capable of distinguishing fresh and properly prepared pan-fried momos from deteriorated or contaminated ones using high-quality image data. The dataset consists of more than 1000 images, including over 500 good-quality pan-fried momo images and 500 bad-quality pan-fried momo images. All images were captured using the Moto G54 5G smartphone, featuring a 50 MP primary camera with Optical Image Stabilization (OIS), an f/1.8 aperture, and Quad Pixel technology, along with an 8 MP ultra-wide and macro vision camera for capturing detailed images from different perspectives. The high-resolution camera preserves important visual features such as texture, color, shape, folds, surface crispiness, frying consistency, and surface defects, which are essential for accurate classification. Dataset Composition: Good Samples (Fresh Pan Fried Momo): The dataset contains more than 500 images of fresh and properly cooked pan-fried momos. These samples exhibit desirable characteristics such as an evenly cooked golden-brown surface, proper shape, crispy texture, fresh appearance, uniform color, and no visible signs of spoilage, contamination, or excessive burning. These images represent the positive class and define the standard of acceptable product quality. Bad Samples (Poor-Quality Pan Fried Momo): The dataset also includes over 500 images of poor-quality pan-fried momos. These samples may show signs of spoilage, fungal growth, discoloration, excessive burning, broken structure, contamination, improper frying, excessive oil absorption, or dryness due to overcooking. These images form the negative class and help the model learn to recognize defective or unsafe food products. Data Collection Setup: Images were captured under controlled conditions using the Moto G54 5G mobile camera. A white background was used to create a clean and uniform contrast between the pan-fried momo samples and the surroundings, making feature extraction more effective. Images were captured under natural daylight with additional 360-degree LED lighting to ensure consistent illumination while minimizing shadows and reflections. This setup resulted in a reliable and high-quality dataset. Image Characteristics: The dataset includes pan-fried momo samples with variations in size, shape, folding style, texture, color, frying level, crispiness, and quality conditions. Data Annotation: Each image was manually labeled as either "Good" or "Bad" based on visual quality assessment through expert observation. L