Image Classification of Chlorine Dispensers Using Neural Networks

Tools Used:
Python, TensorFlow/Keras (or specify your framework),PyTorch, OpenCV (used for image processing), Neural Networks

Description:
Developed an binary image classification model to identify chlorine dispensers using a convolutional neural network (CNN), achieving an accuracy of 85%. The primary objective of this project was to ensure data quality during data collection by verifying that uploaded images are indeed of chlorine dispensers, filtering out irrelevant or incorrect submissions. I preprocessed a dataset of dispenser images—handling tasks such as resizing, normalization, and augmentation—to create a robust training set. Using Python and TensorFlow, I designed and trained a CNN to effectively distinguish between chlorine dispensers and non-dispenser objects, optimizing the model through iterative tuning of hyperparameters and layer architectures. This project showcases my ability to leverage deep learning techniques to address real-world image recognition challenges while demonstrating how AI can enhance data integrity in practical applications.

Outcome:
The model successfully classified chlorine dispensers with 85% accuracy, providing a reliable, automated tool for validating image uploads in real time. By ensuring that only accurate images of chlorine dispensers are included in the dataset, this solution improves the reliability of downstream data analysis and decision-making processes. This highlights the power of AI in maintaining high-quality data standards, particularly in scenarios where manual verification would be time-consuming or prone to human error.