/Object-Detection-YOLOv5

This project demonstrates object detection using YOLOv5. The model is trained on a custom dataset and can detect objects in new images. YOLOv5 is a state-of-the-art object detection model known for its speed and accuracy, making it suitable for real-time applications.

Primary LanguagePython

Object-Detection-YOLOv5

This project demonstrates object detection using YOLOv5. The model is trained on a custom dataset and can detect objects in new images. YOLOv5 is a state-of-the-art object detection model known for its speed and accuracy, making it suitable for real-time applications.

Dataset

You can use any dataset that follows the YOLO format. For this project, we assume the dataset is organized as follows:

Download Dataset

You can download a sample dataset from Roboflow's Public Object Detection Datasets. Extract the dataset into the data/ directory.

Model Architecture

YOLOv5 is a convolutional neural network (CNN) designed for object detection. It divides the image into a grid and predicts bounding boxes and class probabilities for each grid cell.

Installation

  1. Clone the repository:

    git clone https://github.com/SreeEswaran/Object-Detection-YOLOv5.git
cd Object-Detection-YOLOv5

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Download and preprocess the dataset as described above.

Usage

Training

To train the model, run:

python scripts/train.py --config config.yaml

To evaluate the model, run:

python scripts/evaluate.py --config config.yaml

To infer, run

python scripts/infer.py --image_path path/to/your/image.jpg --model_path models/yolov5_model.pth