Good and Bad classification chicken momo

The project titled “Good and Bad Classification of chicken Momo Using iQOO neo 7 pro Mobile Camera” focuses on developing a machine learning and computer vision system to automatically classify chicken momo samples into two categories: good (fresh and high-quality) and bad (stale, spoiled, or poor-quality). The objective of this project is to build an accurate image classification model capable of distinguishing fresh chicken momos from deteriorated or contaminated ones using high-quality image data.
The dataset consists of more than 1000 images, including over 500 good-quality chicken momo images and 500 bad-quality chicken momo images. All images were captured using the IQOO neo 7 pro smartphone, featuring a 50 MP sony primary camera with a 1/1.67-inch sensor, f/1.75 aperture, Optical Image Stabilization (OIS), and Electronic Image Stabilization (EIS). The device also includes 2 MP macro and 2 MP depth sensors, enabling detailed image capture. The high-resolution camera preserves important visual features such as texture, color, shape, folds, and surface defects, which are essential for accurate classification.
Dataset Composition:
Good Samples (Fresh chicken Momo):
The dataset contains more than 500 images of fresh and high-quality chicken momos. These samples exhibit desirable characteristics such as a smooth and uniform surface, proper shape, fresh dough appearance, appropriate color, and no visible signs of spoilage or damage. These images represent the positive class and define the standard of acceptable product quality.
Bad Samples (Poor-Quality chicken Momo):
The dataset also includes over 500 images of poor-quality chicken momos. These samples may show signs of spoilage, fungal growth, discoloration, broken structure, excessive dryness, contamination, or improper cooking. 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 IQOO neo 7 pro mobile camera. A Black background was used to create a clean and uniform contrast between the chicken 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 chicken momo samples with variations in size, shape, folding style, texture, color, and quality conditions. This diversity improves the robustness of the machine learning model and enables it to generalize effectively to different types of momos encountered in real-world situations.
Data Annotation:
Each image was manually labeled as either “Good” or “Bad” based on visual quality assessment by expert observation. These annotations serve as ground truth labels for supervised learning, ensuring accurate model training and evaluation.