Add multi-framework dataset setup for RF-DETR, YOLOX, and YOLOv6

- Create dataset_coco/ for RF-DETR (COCO format)
- Rename dataset_split/ to dataset_yolo/ for clarity
- Add setup_datasets.py script for automated multi-format setup
- Update YOLOv6 script with correct 10-class configuration
- Update README with framework comparison and training instructions
- Update .gitignore to exclude both dataset directories

README.md

@@ -31,7 +31,9 @@ This repository contains a complete wood defect detection system using YOLOX/YOL
 - Valid: 2,027 images
 - Test: 2,029 images
 
-**Format**: YOLO format (images/ and labels/ subdirectories with data.yaml configuration)
+**Formats Available**:
+- `dataset_coco/` → COCO format for RF-DETR
+- `dataset_yolo/` → YOLO format for YOLOX, YOLOv6, YOLOv8
 
 ## 🚀 Quick Start
 
@@ -48,21 +50,19 @@ source .venv/bin/activate
 
 # Install dependencies
 pip install -U pip
-pip install ultralytics gradio
+pip install ultralytics gradio rfdetr
 ```
 
-### 2. Download Dataset
-
-The dataset is not included in the repository due to size. Download from Kaggle and organize as follows:
+### 2. Setup Datasets
 
 ```bash
-# Download from Kaggle (requires Kaggle API)
+# Download dataset from Kaggle (requires Kaggle API)
 kaggle datasets download -d kirs0816/wood-surface-defects
 unzip wood-surface-defects.zip
 
-# Run the dataset preparation script
-python split_coco_dataset.py
-python reorganize_dataset.py
+# Create multi-format datasets
+python split_coco_dataset.py  # Creates dataset_yolo/
+python setup_datasets.py      # Creates dataset_coco/ and updates configs
 ```
 
 ### 3. Launch Annotation GUI
@@ -77,10 +77,38 @@ Open http://localhost:7860 in your browser to access the web-based annotation in
 - Manual annotation tools
 - Real-time result visualization
 
-### 4. Train Model
+### 4. Train Models
 
+Choose from three different frameworks:
+
+#### RF-DETR (Highest accuracy, slower training)
 ```bash
-python train_yolox.py --dataset-dir dataset_split --model yolox-nano --epochs 5 --batch-size 4
+python train_rfdetr.py \
+  --dataset-dir dataset_coco \
+  --output-dir runs/rfdetr_medium \
+  --model medium \
+  --epochs 50 \
+  --batch-size 4 \
+  --grad-accum-steps 4 \
+  --lr 1e-4
+```
+
+#### YOLOX (Balanced performance/speed)
+```bash
+python train_yolox.py \
+  --dataset-dir dataset_yolo \
+  --model yolox-nano \
+  --epochs 50 \
+  --batch-size 8
+```
+
+#### YOLOv6 (Fastest, edge-optimized)
+```bash
+python train_yolov6.py \
+  --dataset-dir dataset_yolo \
+  --model yolov6n \
+  --epochs 50 \
+  --batch-size 8
 ```
 
 ## 📁 Project Structure
@@ -88,11 +116,23 @@ python train_yolox.py --dataset-dir dataset_split --model yolox-nano --epochs 5
 ```
 saw_mill_knot_detection/
 ├── annotation_gui.py          # Gradio web interface for annotation
+├── train_rfdetr.py           # RF-DETR training script
 ├── train_yolox.py            # YOLOX training script
-├── split_coco_dataset.py      # Dataset splitting utility
-├── reorganize_dataset.py      # Dataset reorganization to YOLO format
-├── config.py                  # Configuration settings
-├── dataset_split/             # Training data (excluded from git)
+├── train_yolov6.py           # YOLOv6 training script
+├── setup_datasets.py         # Multi-format dataset setup script
+├── split_coco_dataset.py     # Dataset splitting utility
+├── config.py                 # Configuration settings
+├── dataset_coco/             # RF-DETR dataset (COCO format)
+│   ├── train/
+│   │   ├── *.jpg             # Training images
+│   │   └── _annotations.coco.json
+│   ├── valid/
+│   │   ├── *.jpg             # Validation images
+│   │   └── _annotations.coco.json
+│   └── test/
+│       ├── *.jpg             # Test images
+│       └── _annotations.coco.json
+├── dataset_yolo/             # YOLOX/YOLOv6/YOLOv8 dataset (YOLO format)
 │   ├── train/
 │   │   ├── images/           # Training images
 │   │   └── labels/           # YOLO format labels
@@ -104,17 +144,20 @@ saw_mill_knot_detection/
 │   │   └── labels/           # YOLO format labels
 │   └── data.yaml             # YOLO dataset configuration
 ├── runs/                     # Training outputs (excluded from git)
-│   └── yolox_training/
-│       └── training/
-│           └── weights/
-│               ├── best.pt   # Best model weights
-│               └── last.pt   # Latest model weights
 ├── bbox_coco_dataset.json     # Original COCO annotations
 ├── requirements.txt           # Python dependencies
 ├── .gitignore                # Excludes large data files
 └── README.md                 # This file
 ```
 
+## 🤖 Framework Comparison
+
+| Framework | Accuracy | Speed | Memory | Deployment | Best For |
+|-----------|----------|-------|--------|------------|----------|
+| **RF-DETR** | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | CPU/GPU | Highest accuracy, research |
+| **YOLOX** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | Edge devices | Balanced performance |
+| **YOLOv6** | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | Mobile/Edge | Fast inference, production |
+
 ## 🛠️ Usage Guide
 
 ### Annotation GUI Features