dog-breed-identifier's Introduction

Dog Breed Identifier

This Dog Breed Identifier is a project that leverages the power of deep learning, specifically Convolutional Neural Networks (CNNs), to accurately identify the breed of a dog from an uploaded image. The system is trained on a diverse dataset containing images of five popular dog breeds: Bulldog, Chihuahua, German Shepherd, Golden Retriever, and Husky

REQUIREMENTS

The Dog Breed Identifier requires the following Python libraries:
  TensorFlow
  Keras
  Numpy
  Pillow
  Scipy
  Tkinter
  MobileNetV2 (Pre-trained Model)

OVERVIEW

The project consists of two main components:
    Model Training ('train_model.py)'
        Utilizes TensorFlow and Keras to create a CNN model based on the MobileNetV2 architecture.
        Fine-tunes the model using a dataset containing images of the five dog breeds for training and validation.
    User Interface ('app.py')
        Uses Tkinter for the graphical user interface (GUI).
        Allows users to upload an image of a dog.
        Predicts the breed of the dog using the trained CNN model.
        Displays the predicted breed and its confidence level.

DATASET

The dataset used for training and testing the model includes:
  Training Images: 800 images per breed (total 4,000 images).
  Testing Images: 20 images per breed (total 100 images).
  Validation Images: 20 images per breed (total 100 images).

MODEL PERFORMANCE

After training and evaluation, the model achieved the following accuracies:
  Training Accuracy: 85.62%
  Testing Accuracy: 88.75%
  Validation Accuracy: 89%

OUTPUT

After completing the training, testing, and validation process for the model, the fine-tuned model file named 'fine_tuned_model_mobilenetv2.keras' will be saved in the main root directory of the folder. Run the 'app.py' to run the program.

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