Ad-Creatives-Detection

Image Classification with DeiT

This project implements image classification using the Data-efficient image Transformer (DeiT) model. It includes data preprocessing, k-fold cross-validation, early stopping, and model saving functionalities.

Features:

• Data Preprocessing: Resize, random crop, horizontal flip, and normalization.
• Model Training: K-fold cross-validation with early stopping.
• Model Architecture: Utilizes the DeiT model with configurable parameters.
• Model Evaluation: Option to use a separate test set and save the final trained model.
• Tensorboard Logging: Logs training progress and metrics.
• Configuration: Configurable via a JSON file for easy experimentation.
• Documentation: Includes detailed documentation for classes and functions.

Usage:

1. Update the JSON configuration file as needed.
2. Run the main Python script to start model training and evaluation.

Requirements:

• PyTorch
• torchvision
• timm
• scikit-learn
• tensorboard

File Structure:

• main.py: Main script for model evaluation.
• model_train.py: Main script for training.
• config.json: Configuration file for specifying parameters.
• README.md: Overview of the project and instructions.

License:

This project is licensed under the MIT License.