Malaria Detection Computer Vision MLOps Project

Overview

This project leverages the power of TensorFlow and MLOps practices to develop a state-of-the-art model for detecting malaria from cell images. Utilizing the TensorFlow dataset specifically curated for malaria, this project encompasses a comprehensive workflow including data loading, processing, augmentation, modeling, testing, and integration with MLOps practices using Weights & Biases (Wandb).

TensorFlow in the Project

• Role: TensorFlow is utilized as the primary deep learning framework for this project, enabling the construction and training of complex neural network models.
• Features: We leverage TensorFlow's robust ecosystem, including its comprehensive libraries and tools for data loading, preprocessing, modeling, and training to facilitate efficient and effective model development.
• Data Augmentation: TensorFlow's data augmentation capabilities are extensively used to enhance the dataset, applying various transformations to increase the diversity and size of the training data, which helps improve model robustness.

Data Loading

• Source: TensorFlow Malaria Dataset
• Details: Description of the dataset's structure, size, and specifics of the data points.

Data Processing

• Cleaning: Steps taken to clean and preprocess the data.
• Augmentation: Detailed TensorFlow methods used for data augmentation, including the specific transformations applied to enhance the dataset.

Modeling

• Architecture: Description of the model architecture, including layers, activation functions, and any specific choices made within the TensorFlow framework.
• Training: Outline the training process, highlighting TensorFlow's role in optimizing the training workflow, including batch size, epochs, and optimization strategies.

Testing

• Methodology: Explanation of the testing process within the TensorFlow framework.
• Results: Summary of the testing results, showcasing TensorFlow's capabilities in evaluating model performance.

MLOps with Wandb

• Integration: Details on integrating Weights & Biases with TensorFlow to track experiments, control versions, and tune hyperparameters.
• Usage: Instructions for monitoring the project on Wandb, including links and commands.

Fine Tuning

• Approach: Elaboration on the fine-tuning process using TensorFlow's tools and techniques.
• Results: Improvements achieved through fine-tuning, demonstrating TensorFlow's effectiveness in model optimization.

Getting Started

Instructions for setting up the TensorFlow environment and other project dependencies: