YOLOvs-Image-Classification

Project Overview

This project uses the YOLOv5 object detection model and PyTorch to detect objects in an image. The model identifies objects, draws bounding boxes around them, displays confidence scores, and saves the final output image.

The project demonstrates the practical use of open-source computer vision technology for image analysis and object detection.

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Features

• Detects objects in an image using YOLOv5
• Draws bounding boxes around detected objects
• Displays confidence scores
• Prints detected object names
• Prints the total number of detected objects
• Saves the output image automatically

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Technologies Used

• Python
• PyTorch
• YOLOv5
• OpenCV
• NumPy
• Matplotlib

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Project Modifications

The following modifications were made to the original code:

1. Changed the confidence threshold to 0.30
2. Printed the total number of detected objects
3. Printed detected object names and confidence scores
4. Added comments to explain important code sections

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Input Image

The project uses a custom image provided by the user.

File:

input_image.jpg

The image contains multiple objects that can be detected by the YOLOv5 model.

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How to Run

Clone the Repository

git clone https://github.com/YOUR_USERNAME/YOLOvs-Image-Classification.git

Navigate to the Project Folder

cd YOLOvs-Image-Classification

Install Dependencies

pip install -r requirements.txt

Run the Program

python main.py

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Example Output

Total detected objects: 5

person: 0.91
chair: 0.88
laptop: 0.84
bottle: 0.79
backpack: 0.75

Replace the above output with your actual detection results.

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Output Result

The detected image is saved as:

output_result.png

The output image contains:

• Bounding boxes
• Object labels
• Confidence scores

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Project Structure

YOLOvs-Image-Classification/
│
├── main.py
├── input_image.jpg
├── output_result.png
├── README.md
├── requirements.txt
├── LICENSE
└── report.docx

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Why README.md and requirements.txt Are Important

README.md

README.md provides information about the project, including its purpose, features, setup instructions, and usage. It helps other users understand and use the project easily.

requirements.txt

requirements.txt contains all required Python libraries. It allows users to install the necessary dependencies quickly and run the project without configuration issues.

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License

This project is licensed under the MIT License.

See the LICENSE file for details.

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Author

Tanim Ahammed

Open Source Software Course

Sejong University