Project Report: Ageing Signs Localization

Dependencies:

• Software:
  • Jupyter Notebook
  • Google Colab
• Packages and Libraries:
  • OS
  • CV2
  • Keras
  • Numpy
  • TensorFlow
  • Matplotlib
  • Argparse
  • EfficientnetB0

Steps Involved in Project:

1. Importing Dependencies and Dataset:

   • Importing all the required libraries and packages.
   • Importing the dataset.

2. Data Preparation:

   • Splitting the dataset into target and training data.

3. Image Preprocessing:

   • Converting color images into black and white using the map function.

4. Data Augmentation:

   • Performing data augmentation to increase the diversity of the dataset.

5. One-Hot Encoding:

   • Using one-hot encoding to increase the number of neurons.

6. Model Architecture:

   • Specifying the architecture of the model using EfficientNetB0.

7. Model Compilation:

   • Compiling the model.

8. Model Training:

   • Training the model with a batch size of 30 and epochs set to 30.

9. Testing:

   • Testing the model to localize ageing signs with an accurate percentage.

Steps to Run the Code:

1. Download the zip folder.
2. Extract the zip folder.
3. Open the Jupyter Notebook.
4. Run the Om-Preetham-Bandi-AgeingSign-Batch3.ipynb Jupyter notebook.
5. Load the respective models and their corresponding weights.
6. Change the image_path variable to the path of the image file that you want to test on.
7. Test on the image file.