Pakistani Celebrity Image Classifier CNN

This project involves building a Convolutional Neural Network (CNN) to classify images of Pakistani celebrities. The dataset was collected using the Chrome Extension "Download All Images" and underwent several preprocessing steps before model training.

Libraries/Dependencies:

1. OpenCV (cv2)
2. NumPy
3. Matplotlib
4. os
5. shutil
6. TensorFlow
7. Keras
8. imghdr

Project Workflow:

1. Importing Libraries:

All necessary libraries are imported at the beginning of the script for clarity and reproducibility.

2. Data Cleaning:

Data cleaning is essential for preparing the dataset for training. This step involves:

• Utilizing pre-trained Haar Cascade XML files to detect and crop faces in images.
• Deleting images where the cropped face does not belong to the intended celebrity.
• Removing blurry or low-quality images that may hinder model performance.

3. Image Preprocessing:

A preprocessing function is applied to handle images with incorrect formats and ensure uniformity in the dataset.

4. Data Pipeline:

The dataset is processed into batches using TensorFlow's Keras utilities to streamline model training.

5. Image Scaling:

Images are scaled to a consistent size to facilitate model training, as CNNs typically require inputs of uniform dimensions.

6. Data Splitting:

The dataset is divided into training, testing, and validation sets with proportions of 70%, 20%, and 10%, respectively.

7. Model Building:

The CNN architecture consists of:

• Six convolutional (Conv2D) layers followed by max-pooling layers for feature extraction.
• A flattening layer to transform the data into a 1D array.
• Two dense (Dense) layers for classification, with the output layer using the softmax activation function for multi-class classification. The model is compiled with sparse categorical cross-entropy loss and the Adam optimizer.

8. Performance Evaluation:

The model demonstrates high accuracy, achieving 98.44% accuracy with minimal loss (0.06).

Screenshots:

Model Architecture:

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Importing the Repository to Local Environment and Usage:

1. Clone the Repository:

git clone https://github.com/your-username/your-repository.git

2. Navigate to the Repository:

cd your-repository

3. Install Dependencies:

pip install -r requirements.txt

4. Run the Script:

python main.py