Dog Breed Identifier

Project Description

This project utilizes pre-trained convolutional neural networks (CNNs) to classify images of dogs and identify their breeds. The primary objectives are:

1. To accurately distinguish between dog and non-dog images.
2. To classify the breeds of the identified dogs.

The project employs popular CNN architectures such as VGG, AlexNet, and ResNet to achieve high accuracy and efficiency in image classification.

Table of Contents

• Installation Instructions
• Usage
• Results
• Contributing
• License
• Contact Information
• Additional Resources

Installation Instructions

1. Clone the repository:

   git clone https://github.com/bhaveshasasik/dog_image_classifier.git

2. Navigate to the project directory:

   cd dog_image_classifier

3. Install the required packages:

   pip install -r requirements.txt

Usage

To classify images, run the following command:

python check_images.py --dir pet_images/ --arch vgg --dogfile dognames.txt

Parameters:

• --dir: Specify the directory containing the pet images (e.g., pet_images/).
• --arch: Choose the CNN architecture to use (options: vgg, alexnet, resnet).
• --dogfile: Specify the file containing the list of valid dog breeds (e.g., dognames.txt).

Results

The project achieved the following results for dog breed classification:

VGG Model

• Accuracy in breed classification: 93.3%
• Correctly classified dog images: 100%
• Correctly classified non-dog images: 100%

AlexNet Model

• Accuracy in breed classification: 80.0%
• Correctly classified dog images: 100%
• Correctly classified non-dog images: 100%

ResNet Model

• Accuracy in breed classification: 90.0%
• Correctly classified dog images: 100%
• Correctly classified non-dog images: 90%

License

This project is licensed under the MIT License.

Contact Information

For questions or feedback, feel free to reach out:

• Email: [email protected]
• GitHub: Bhavesha Sasikumar