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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.