ElectroCom61: A Multiclass Dataset for Detection of Electronic Components
Description
The "ElectroCom61" dataset contains 2071 annotated images of electronic components sourced from the Electronic Lab Support Room, the United International University (UIU). This dataset was specifically designed to facilitate the development and validation of machine learning models for the real-time detection of electronic components. To mimic real-world scenarios and enhance the robustness of models trained on this data, images were captured under varied lighting conditions and against diverse backgrounds. Each electronic component was photographed from multiple angles, and following collection, images were standardized through auto-orientation and resized to 640x640 pixels, introducing some degree of stretching. The dataset is organized into 61 distinct classes of commonly used electronic components. The dataset were split into training (70%), validation (20%), and test (10%) sets.
Files
Steps to reproduce
The directory paths for training, validation, and testing datasets are specified in a configuration file named "data.yaml", ensuring that anyone replicating the study can easily locate and use the data.