ChemEq25 :- A Novel Multiclass Dataset for Detecting Chemical Laboratory Apparatus Using Image Processing and Deep Learning Approach

The "ChemEq25" dataset comprises 4,599 annotated images of chemical laboratory equipment, collected from three locations: Bangladesh Atomic Energy Chemical Lab, Biochemical Laboratory of United International University, and Chemistry Laboratory of Dhaka Imperial College. Designed for machine learning applications, it supports real-time equipment detection. Images were captured under varying lighting and backgrounds to enhance model robustness. Each item was photographed from multiple angles, auto-oriented, and resized to 640x640 pixels, introducing slight stretching. The dataset includes 25 classes of common lab equipment, divided into training (70%), validation (20%), and test (10%) sets.

“ChemEq25”数据集包含4599张带标注的化学实验室设备图像，采集自三处地点：孟加拉国原子能化学实验室、联合国际大学生物化学实验室以及达卡帝国学院化学实验室。本数据集专为机器学习应用研发，可支持实时设备检测任务。为增强模型鲁棒性，图像采集于不同光照与背景条件下。每类设备均从多个角度拍摄，并经过自动定向处理，统一调整至640×640像素分辨率，过程中存在轻微拉伸现象。该数据集涵盖25类常见实验室设备，划分为训练集（占比70%）、验证集（占比20%）与测试集（占比10%）。