Product Classification with Computer Vision

This repository contains a Python program for product classification using a classical computer vision model. The purpose of this project is to classify products into specific categories based on images provided.

Overview

The program follows these main steps:

1. Data Preparation: It starts with organizing the data into folders where each category has training and validation subfolders containing images of specific products.
2. Feature Extraction: Utilizes Histogram of Oriented Gradients (HoG) for feature extraction from images in the training folders of each category.
3. Model Training: Applies Support Vector Machine (SVM) machine learning classifier using the extracted features for training.
4. Model Evaluation: Tests the trained model using images from the validation folders of each category and computes validation accuracy.

Issues Addressed

Overfitting

The initial model suffered from overfitting, evident from the 100% training accuracy and lower validation accuracy (86%). To mitigate this issue, the following steps were taken:

1. Data Augmentation: Techniques like data augmentation were applied to increase the diversity of training data, thereby reducing overfitting.
2. Cross Validation: Employed cross-validation techniques to ensure the model's generalizability and robustness.
3. Parameter Tuning: Explored various SVM parameters to optimize the model's performance.

Results

After implementing the aforementioned strategies, the validation accuracy increased to 89%, indicating improved performance in classifying products. However, issues with training accuracy still persist and require further investigation and refinement.

Repository Structure

• Product Classification /: Contains the dataset organized into categories with training and validation images.
• main.py/: Source code for the product classification program .
• controlledAugmentation.py: Script for augmenting dataset using specified values for each augmentation property.
• randomAugmentation.py: Script for augmenting dataset randomly -hopefully it results a good accuracy-.
• program.png: image for the program after running it.
• README.md: Overview and instructions for the repository.

Usage

1. Ensure Python and required libraries are installed.
2. Organize your dataset into categories with training and validation subfolders.
3. Run the scripts in the main.py/.

Requirements

• Python (version >= 3.0)
• OpenCV
• scikit-learn
• NumPy
• tkinter

Contributors

• Mohamed Amir
• Ahmed Abdelaziz

License

This project is licensed under the MIT License.