# AppleNMR-MM Project Package

This compressed package contains all the necessary files, datasets, and code to reproduce the experiments described in the AppleNMR-MM project. Below is an overview of the folder and file structure:

## 1. `requirements.txt`
This file lists all the required Python packages and versions for running the project. Please install them using:

```bash
pip install -r requirements.txt
```

## 2. `Appendix.pdf`
This document provides supplementary information for the main research paper, including extended tables, detailed methodology, and additional results.

## 3. `dataset/`  
This folder contains the datasets used in the project, organized into three subdirectories:

- `Appledata V1.0/`: Contains the `YOLODataset` used for training the YOLO-based apple region detection model in the Appledata V1.0 pipeline.
- `MNR-Apple-LLM/`: Includes three subfolders:
  - `Baseline_classification_text/`: Datasets used for text-only classification baseline experiments.
  - `Baseline_classification_vit/`: Datasets used for vision-only classification baseline experiments.
  - `Multi-modal model test/`: Datasets used for multimodal fusion and ablation experiments.
- `AppleNMR-Chat-Expert/`: Contains the curated multi-turn dialogue dataset used to train the AppleNMR-Chat-Expert conversational model.

## 4. `code/`  
This folder contains all the source code for the project, organized into three modules corresponding to the dataset folder structure:

- `Appledata V1.0/`:  
  - `yolo_results/`: Stores the output results from four different YOLO model versions.
  - Four Python scripts for preprocessing, YOLO-based detection, and batch training.

- `MNR-Apple-LLM/`:  
  - `Baseline_classification_text/`: Code for text-only classification baseline models.
  - `Baseline_classification_vit/`: Code for vision-only classification baseline models.
  - `Multi-modal model test/`: Code for multimodal fusion and ablation experiments.

- `AppleNMR-Chat-Expert/`:  
  - Source code for training the AppleNMR-Chat-Expert dialogue model using expert-validated multi-turn conversations.

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