---
language: en
license: mit
tags:
- yolo
- ultralytics
- object-detection
- defect-detection
- industrial-inspection
---

# Cosmetic Defect Detection (YOLOv8)

This model is a YOLOv8-based object detection model trained to identify cosmetic defects on metal surfaces.

## Model Details
- **Architecture**: YOLOv8n (Weights: `best.pt`)
- **Task**: Object Detection
- **Classes**:
  - `Crazing`
  - `Inclusion`
  - `Patches`
  - `Pitted`
  - `Rolled-in Scale`
  - `Scratches`

## Training Results
The model was trained on the **Metal Surface Defect Dataset (NEU)**. Training results, including confusion matrices and performance plots, are available as files in this repository.

### Performance
- **Confusion Matrix**: See `confusion_matrix.png`
- **Results Plot**: See `results.png`

## How to use
You can load this model using the `ultralytics` library:

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download the model weights
model_path = hf_hub_download(repo_id="Ashgibbs/Cosmetic_Defect_Detection", filename="best.pt")

# Load the model
model = YOLO(model_path)

# Run inference
results = model.predict("path/to/image.jpg")
results[0].show()
```

## Dataset Credit
The training was conducted using the NEU Surface Defect Dataset.