This is my personal image classification project aimed at identifying landmarks in the Philippines. The goal of this project is to build an accurate image classifier that can recognize and classify images of landmarks found in the Philippines.
I aim for the project to have the following components:
🟩 Web Scraping: Code to scrape images of landmarks from relevant websites, such as tourism websites, travel blogs, or photography platforms.
🟩 Data Processing: Scripts to organize and preprocess the scraped images, including filtering and validating the images to remove irrelevant or poor-quality ones. The dataset will be organized into appropriate classes based on the landmarks or objects depicted.
🟩 Model Training: Implementation of image classification models using popular deep learning frameworks like TensorFlow or PyTorch.
🟩 Evaluation and Testing: Code to evaluate the performance of the trained models using appropriate metrics and techniques. The model will be tested on a separate test dataset to measure its accuracy and generalization capabilities.
🟩 Documentation and Resources: Supplementary materials, such as guidelines for dataset annotation, a README file explaining the project structure, and any additional resources or references that might be helpful for understanding and reproducing the project.