Image Processing Project : Smoker Detection

Main Problem-

The main problem is to classify images into two categories: smokers and non

smokers. The dataset consists of labeled images representing these two

groups. The challenge lies in accurately distinguishing between the two classes

using a machine learning model.

Solution-

To solve this problem, we implemented a Convolutional Neural Network

(CNN) model. By leveraging a pre-trained model (Xception) and fine

tuning it for our dataset, we aim to achieve high accuracy and reliable

classification performance.

Tools Used

Libraries:

● NumPy: For numerical operations.

● Matplotilb: For data visualization.

● OpenCV: For image manipulation.

● Scikit-learn: For data manipulation and preprocessing.

● TensorFlow/Keras: For building and training the neural network.

Techniques:

● Data preprocessing to enhance image quality.

● Data augmentation to improve model generalization.