Deep Learning Flower Classification

This repository contains implementations of various deep learning models for classifying flower images.

Models Included

The following models are implemented in this repository:

• DenseNet: DenseNet/
• EfficientNet: EfficientNet/
• GoogLeNet: GoogLeNet/
• Transformer: Transformer/

Data

The project uses flower image data located in:

• data/flower_data/: Processed data for training and validation.
• data/flower_photos/: Original flower photos.

Note: Data and pre-trained model weights are excluded from the repository via the .gitignore file.

Project Structure

Each model resides in its own directory (e.g., DenseNet/, EfficientNet/). Within each model's directory, you will typically find:

• model.py: Defines the model architecture.
• train.py: Script for training the model.
• evaluate.py: Script for evaluating the trained model.
• predict.py: Script for making predictions on new images.
• my_dataset.py: Defines the custom dataset loading logic.
• utils.py: Contains utility functions.
• class_indices.json: Maps class indices to class names.

Usage

1. Clone the repository:

   git clone https://github.com/xixu-me/deep-learning-flower-classification.git
   cd deep-learning-flower-classification

2. Prepare Data: Ensure the required datasets are present in the data/ directory as expected by the scripts.

3. Install Dependencies: Install necessary Python libraries (e.g., PyTorch, torchvision, numpy, matplotlib). Consider adding a requirements.txt file.

4. Navigate to a Model Directory:

   cd DenseNet/ # or EfficientNet/, GoogLeNet/, Transformer/

5. Train:

   python train.py # Add necessary arguments

6. Evaluate:

   python evaluate.py # Add necessary arguments

7. Predict:

   python predict.py --image_path <path_to_image> # Add necessary arguments

Refer to the specific scripts within each model directory for detailed usage instructions and available arguments.

License

Copyright © Xi Xu. All rights reserved.

Licensed under the GPL-3.0 license.