Convolutional Neural Network (CNN) Project

Overview This repository contains code for a Convolutional Neural Network (CNN) project. The project aims to implement and train a CNN model for image classification tasks. It includes scripts for data preprocessing, model training, evaluation, and inference.

Requirements

Python (>=3.6)

TensorFlow (>=2.0) or PyTorch (>=1.0)

NumPy

Matplotlib (for visualization, optional)

Pandas (for data manipulation, optional)

Data Preprocessing: Prepare your dataset for training by preprocessing images and labels. You can modify preprocess_data.py according to your dataset structure and preprocessing requirements.

Model Training: Train your CNN model using the preprocessed data. You can choose between TensorFlow and PyTorch implementations by running train_tf.py or train_pytorch.py, respectively. Modify these scripts to adjust hyperparameters, network architecture, etc.

Evaluation: Evaluate the trained model's performance using validation or test data. Run evaluate.py and specify the path to the trained model checkpoint.

Inference: Use the trained model for making predictions on new data. Modify infer.py to load the trained model and provide input images for inference.

Project Structure data/: Contains data processing scripts and directories for raw and preprocessed data. models/: Contains CNN model implementations (TensorFlow and PyTorch versions). utils/: Utility functions for data loading, visualization, etc. train_tf.py: Script for training CNN model using TensorFlow. train_pytorch.py: Script for training CNN model using PyTorch. evaluate.py: Script for model evaluation. infer.py: Script for inference on new data. requirements.txt: List of Python dependencies. Contributing Contributions are welcome! If you find any bugs or have suggestions for improvement, please open an issue or submit a pull request.