This repository contains a fine-tuned YOLOv11 model for detecting various electronics components. The model is trained on the ElectroCom61 Dataset and can be used to detect components such as resistors, capacitors, transistors, and more in images.
The goal of this project is to create a deep learning model capable of accurately identifying and localizing electronic components within images. Leveraging YOLOv11's state-of-the-art architecture, the model can detect multiple component types with high precision, making it suitable for use in automated inspection and quality control for electronics manufacturing.
The model was trained on the ElectroCom61 Dataset, which includes labeled images of various components with over 61 classes. Each component is annotated with bounding boxes and class labels, facilitating object detection tasks.
The dataset includes the following classes (components):
- Fuse
- Capacitors
- Humidity-Sensor
- Water-Sensor
- Arduino-Nano
- LCD-Display
- And more...
To use this project, ensure you have Python and the necessary dependencies installed. Follow these steps:
-
Clone the repository:
git clone https://github.com/Tejax-v2/Electronics-Components-Detection.git cd Electronics-Components-Detection -
Install dependencies:
pip install -r requirements.txt
-
Dataset Preparation: Ensure the ElectroCom61 Dataset is in the working directory. Follow any preprocessing instructions if necessary.
The model is trained using YOLOv11, with adjustments to hyperparameters specific to detecting electronic components.
Follow the notebook YOLOv11_Object_Detection.ipynb for further instructions
- Dataset: ElectroCom61
- Reference: Roboflow
- Model: Ultralytics