Shelf life determination of Hypophthalmicthys molitrix ( Silver Carp )

This project, titled "Good and Bad Classification of Silver Carp(Hypophthalmichthys molitrix) is designed to develop an image classification system that distinguishes between healthy (good) and unhealthy (bad) Silver Carp fish (Hypophthalmichthys molitrix). The dataset consists of approximately 2000 images, evenly distributed between good and bad samples. All images were captured using a Realme 5i mobile camera, providing high-resolution visual data suitable for machine learning applications. The fish were photographed against a black background in daylight conditions to ensure consistency, clarity, and accurate feature capture.
Dataset Composition
Good Samples (Healthy)
The dataset includes approximately 1000 images of healthy Silver Carp fish. These images show fish with:
Bright, shiny, and intact scales
Clear, transparent eyes
Proper body shape without deformities
Natural coloration and smooth texture
These samples represent the positive class and help train the model to recognize healthy fish conditions.
Bad Samples (Unhealthy)
The dataset also contains approximately 1000 images of unhealthy Silver Carp fish. These fish may exhibit:
Dull or discolored scales
Cloudy or damaged eyes
Physical deformities
Visible injuries or infections
Poor overall physical condition
These images represent the negative class, enabling the model to identify unhealthy fish accurately.
Data Collection Setup
All images were captured using a Realme 5i smartphone camera, known for its reliable image quality and resolution. A black background was used intentionally to:
Enhance contrast between the fish and the background
Reduce noise and unwanted visual distractions
Highlight important visual features such as scales, eyes, and body structure
Images were taken under natural daylight conditions, ensuring consistent illumination and accurate representation of color and texture.
Image Characteristics
The dataset includes variations in:
Fish size
Body orientation
Color intensity
Health condition
This diversity improves the robustness of the machine learning model and ensures better performance in real-world scenarios.
Data Annotation
Each image is carefully labeled as either:
"Good" (Healthy)
"Bad" (Unhealthy)
These labels serve as the ground truth, allowing the machine learning model to learn the differences between healthy and unhealthy fish accurately.

本项目命名为“白鲢（Hypophthalmichthys molitrix）健康与不健康状态分类”，旨在开发一套图像分类系统，用于区分健康（优质）与不健康（劣质）的白鲢。本数据集共包含约2000张图像，优质与劣质样本数量均衡。所有图像均使用Realme 5i手机摄像头拍摄，提供适用于机器学习应用的高分辨率视觉数据。拍摄时鱼体置于黑色背景下，并采用自然光照明，以保障拍摄一致性、画面清晰度与特征的精准捕捉。

数据集构成
优质样本（健康个体）
本数据集包含约1000张健康白鲢图像，此类图像中的鱼体具备以下特征：鳞片光亮完整、眼部清澈透明、体型正常无畸形、体色自然且质感平滑。此类样本作为正类，用于训练模型识别健康鱼体状态。

劣质样本（不健康个体）
本数据集同时包含约1000张不健康白鲢图像，此类鱼体可能表现出以下特征：鳞片暗淡或变色、眼部浑浊或受损、存在躯体畸形、可见损伤或感染、整体健康状况不佳。此类图像作为负类，帮助模型精准识别不健康鱼体。

数据采集设置
所有图像均使用Realme 5i智能手机摄像头拍摄，该设备具备可靠的成像质量与分辨率。刻意采用黑色背景的目的在于：增强鱼体与背景的对比度、降低噪声与无关视觉干扰、突出鳞片、眼部及躯体结构等关键视觉特征。图像采集全程使用自然光照明，以保证光照一致性，精准还原鱼体色彩与质感。

图像特征
本数据集涵盖以下维度的多样性：鱼体尺寸、躯体朝向、色彩强度、健康状态。此类多样性可提升机器学习模型的鲁棒性，确保其在真实场景中具备更优异的表现。

数据标注
每张图像均被精准标注为"优质"（健康）或"劣质"（不健康），此类标签作为真实标签（ground truth），使机器学习模型能够精准学习健康与不健康鱼体之间的差异。