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ResNet & ResNeXt Image Classification Project

This repository contains three Python scripts for training, evaluating, and fine-tuning deep learning models based on ResNet-50 and ResNeXt-50 architectures using TensorFlow/Keras. The goal of the project is to classify rock images into five different geological classes.

Repository Structure

├── pre_ResNet-50.py
├── pre_ResNeXt50.py
├── retrain_resnet_resnext.py
├── README.md
└── LICENSE

File Descriptions

1. pre_ResNet-50.py

This script performs initial training (transfer learning) using a pre-trained ResNet-50 model.

Key features:

  • Uses ImageNet pre-trained weights
  • Image data augmentation (shear, zoom, horizontal flip)
  • Custom fully connected layers
  • SGD optimizer
  • Accuracy and loss visualization
  • Confusion matrix computation

Classification classes:

  • intact Rock
  • stylolite
  • horizontal plug
  • vertical plug
  • Crack

2. pre_ResNeXt50.py

This script is similar to the ResNet version but uses ResNeXt-50 as the backbone network.

Main differences from ResNet-50:

  • Higher cardinality architecture
  • Improved feature representation capability

Other steps include:

  • Data augmentation
  • Training with SGD optimizer
  • Model evaluation and confusion matrix visualization

3. retrain_resnet_resnext.py

This script is designed for fine-tuning previously trained ResNet or ResNeXt models.

Capabilities:

  • Dynamic learning rate scheduling (LearningRateScheduler)
  • SGD with momentum
  • Dropout layers to reduce overfitting
  • Multiple fine-tuning strategies (commented and optional)

Fine-tuning options included:

  • Freezing selected base model layers
  • Switching optimizers (e.g., Adam)
  • Advanced data augmentation
  • Adding additional dense layers

Requirements

  • Python 3.8+
  • TensorFlow 2.x
  • NumPy
  • Matplotlib
  • Seaborn
  • scikit-learn
  • Google Colab (recommended)

Install dependencies:

pip install tensorflow numpy matplotlib seaborn scikit-learn

Dataset Structure

The dataset must be organized as follows (Google Drive):

DataSplit/
├── train/
│   ├── intact Rock/
│   ├── stylolite/
│   ├── horizontal plug/
│   ├── vertical plug/
│   └── Crack/
└── validation/
    ├── intact Rock/
    ├── stylolite/
    ├── horizontal plug/
    ├── vertical plug/
    └── Crack/

How to Run

  1. Open the desired script in Google Colab
  2. Mount Google Drive
  3. Verify dataset paths
  4. Run the script

Outputs

  • Training and validation accuracy plots
  • Training and validation loss plots
  • Final validation accuracy
  • Confusion matrix for model performance analysis

📜 License

This project is released under the MIT License.
You may freely use and adapt the code for research and educational purposes.

See the LICENSE file for full details.