Chess Pieces Recognition using Deep Learning

Project Overview

Chess Pieces Recognition is a deep learning project focused on classifying chess pieces from images using the powerful InceptionResNetV2 architecture. This project incorporates transfer learning techniques, leveraging pre-trained weights on ImageNet for feature extraction. The model is trained on a diverse dataset of chess pieces, and its performance is evaluated using various metrics, including accuracy, loss, confusion matrix, and a detailed classification report.

Key Features

• Transfer Learning with InceptionResNetV2:

  • Utilizes the InceptionResNetV2 architecture for effective transfer learning and feature extraction.

• Data Augmentation:

  • Implements data augmentation techniques such as rotation, zooming, shifting, and flipping to enhance model generalization.

• Visualization and Analysis:

  • Generates visualizations for training and validation metrics, aiding in the analysis of model performance over epochs.

• Early Stopping:

  • Implements early stopping to prevent overfitting during model training.

• Comprehensive Evaluation:

  • Evaluates the model using metrics like accuracy, loss, confusion matrix, and a detailed classification report.

Tech Stack

• Deep Learning Framework:

  • TensorFlow and Keras are employed for developing the deep learning model.

• Transfer Learning Backbone:

  • InceptionResNetV2 is used as the backbone architecture for transfer learning.

• Data Preprocessing:

  • ImageDataGenerator is applied for data preprocessing and augmentation.

Usage

To replicate and run the project on your local machine, follow these steps:

1. Clone the repository:

   git clone (https://github.com/rakesh-vajrapu/chess-pieces-recognition).git

2. Install dependencies: pip install -r requirements.txt
3. Run the project: python chess_pieces_recognition.py