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🐶 Dog Breed Prediction Model

This project focuses on multi-class image classification, where the goal is to predict different breeds of dogs using deep learning. We leverage TensorFlow and Transfer Learning to classify images into multiple dog breeds with high accuracy.

📌 Project Overview

This deep learning project applies a convolutional neural network (CNN) to classify dog breeds. Using a pretrained model from TensorFlow Hub, we fine-tune the model to recognize and differentiate multiple breeds efficiently.

The workflow follows the standard deep learning pipeline:

  1. Getting the Data Ready 🗂️

    • Import and store dataset
    • Load images and labels for training

  2. Preparing the Data 🔄

    • Convert images into numerical tensors
    • Split images and labels (X, y)
    • Create a custom validation set
    • Convert image tensors into tensor tuples
    • Generate data batches of size 32

  3. Choosing and Training the Model 🎯

    • Define input and output layers
    • Use a pretrained deep learning model from TensorFlow Hub (transfer learning)
    • Train the model using GPU for faster processing

  4. Evaluating the Model 📊

    • Make predictions on new images
    • Compare predictions with ground truth labels
    • Analyze model performance using accuracy and loss metrics

  5. Improving the Model 🚀

    • Experiment with different model architectures
    • Fine-tune hyperparameters
    • Optimize training strategies for better performance

  6. Saving, Sharing, and Reloading the Model 💾

    • Save trained model for later use
    • Reload model for future inference tasks

📂 Dataset

The dataset consists of images of multiple dog breeds, categorized into different classes. Each image is labeled with its respective breed, serving as the ground truth for training and evaluation.

🖥️ Google Colab & GPU Usage

This project was implemented using Google Colab, taking advantage of GPU acceleration for faster training. Running deep learning models on GPU significantly reduces training time compared to CPUs.

🔧 Setup

Key libraries used:

  • TensorFlow 2.x: For deep learning and model training
  • TensorFlow Hub: For pretrained models (transfer learning)
  • Matplotlib & Seaborn: For data visualization
  • NumPy & Pandas: For data manipulation

📈 Model Performance

The model is evaluated using standard metrics:

  • Accuracy: Measures the correct classification rate
  • Loss: Evaluates model errors