# YOLOv8 Training & Testing Guide

This guide details how to prepare your dataset, train the YOLOv8 model for Potholes and Road Signs, and test the trained model.

## 1. Dataset Preparation

YOLOv8 requires data in a specific format.

### A. Data Structure

Organize your dataset folder like this:

```
datasets/
  road_signs_potholes/
    train/
      images/
        img1.jpg
        ...
      labels/
        img1.txt
        ...
    val/
      images/
        ...
      labels/
        ...
    data.yaml
```

### B. Label Format

Each `.txt` file in `labels/` corresponds to an image.
Format: `class_id center_x center_y width height` (normalized 0-1).

Example `img1.txt`:

```
0 0.5 0.5 0.2 0.3
1 0.1 0.1 0.05 0.1
```

**Class IDs Mapping (Example):**

- `0`: Traffic Sign
- `1`: Pothole
- `2`: Manhole

### C. Creating `data.yaml`

Create a `data.yaml` file inside your dataset folder (or anywhere accessible).

```yaml
path: ../datasets/road_signs_potholes # dataset root dir
train: train/images # train images (relative to 'path')
val: val/images # val images (relative to 'path')

# Classes
names:
  0: Traffic Sign
  1: Pothole
  2: Manhole
```

## 2. Training the Model

We have provided a script `backend/models/train_yolo.py`.

**Command:**
Open your terminal in `d:\Time-Pass-Projects\pothole-roadsign detection`.

```bash
# Activate your environment if needed
python backend/models/train_yolo.py
```

_Note: You will need to edit `backend/models/train_yolo.py` slightly to point to your actual `data.yaml` path if you haven't already, or pass it as an argument if you modify the script to accept args._

**Training Process:**

1.  The script downloads `yolov8n.pt` (nano) as a starting point.
2.  It runs for 50 epochs (adjustable).
3.  **Result:** Weights are saved in `runs/detect/train/weights/best.pt`.

## 3. Testing the Model

Once trained, you should test it visually.

### A. Locate your specific model

Find `runs/detect/trainX/weights/best.pt`.
Copy this file to `backend/models/best.pt` (or update paths in scripts).

### B. Run the Test Script

We have created `backend/test_model.py` for quick verification.

```bash
python backend/test_model.py
```

_Make sure to update the `video_path` or `image_path` in `test_model.py` to point to a real file._

## 4. Integration

After you are satisfied with `best.pt`:

1.  Move `best.pt` to `backend/models/`.
2.  Updates `backend/pipelines/video_processor.py` line 10:
    ```python
    self.yolo = YOLOManager("backend/models/best.pt")
    ```