/Chromatic-Challenge-Noise-Effects-on-CNN-Color-Classification

Explore the effects of noise on CNN color classification in this repository. Discover how Convolutional Neural Networks respond to variations in image quality caused by different levels of noise. Dive into the code, dataset, visualizations, and findings of our investigation. Join us in unraveling the impact of chromatic chaos on color recognition.

Primary LanguageJupyter Notebook

Chromatic-Challenge-Noise-Effects-on-CNN-Color-Classification

Explore the effects of noise on CNN color classification in this repository. Discover how Convolutional Neural Networks respond to variations in image quality caused by different levels of noise. Dive into the code, dataset, visualizations, and findings of our investigation. Join us in unraveling the impact of chromatic chaos on color recognition.

Introduction

Welcome to the Chromatic Challenge repository! This project aims to explore the intriguing world of color classification using Convolutional Neural Networks (CNNs) in the presence of various noise types and levels. Colors play a pivotal role in image recognition and understanding how noise impacts CNN-based color classification models can provide valuable insights into their robustness and reliability.

Motivation

With the increasing reliance on computer vision systems in real-world applications, understanding the vulnerabilities of these systems is crucial. Noise, in the form of sensor inaccuracies, compression artifacts, and environmental factors, is an inevitable aspect of image data. This project delves into the impact of noise on CNN models trained for color classification tasks.

Key Objectives

  • Noise Generation: Implementing various noise types such as Gaussian noise, salt-and-pepper noise, and speckle noise to mimic real-world scenarios.
  • Dataset Preparation: Curating a diverse color dataset that covers a wide spectrum of colors and scenarios.
  • CNN Architecture: Designing and implementing a robust CNN architecture suitable for color classification.
  • Experimental Analysis: Training the CNN model on the clean dataset and evaluating its performance under different noise conditions.
  • Noise Mitigation Strategies: Exploring techniques to enhance the model's noise robustness, such as data augmentation, transfer learning, and denoising layers.
  • Results and Insights: Documenting and analyzing the experimental results to draw meaningful conclusions about the impact of noise on CNN color classification and the effectiveness of noise mitigation strategies.

Repository Contents

  • Code: The codebase for generating noise, preparing datasets, building and training CNN models, and conducting experiments.
  • Datasets: Clean color datasets and noisy variants generated by applying different noise types and intensities.
  • Pre-trained Models: Pre-trained CNN models, both with and without noise mitigation strategies applied.
  • Documentation: In-depth explanations of the codebase, dataset structure, CNN architecture, and experimental procedures.
  • Results and Analysis: Visualizations, graphs, and insights drawn from the experimental outcomes.
  • Contributing: Guidelines for external contributors who wish to collaborate on the project.

Getting Involved

Contributions, suggestions, and collaborations are highly encouraged!

URL of Web Application

https://chromatic-challenge-noise-effects-on-cnn-color-classification.streamlit.app/