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title: Metal Surface Defect Detection - YOLOv8
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
---
# 🔍 Metal Surface Defect Detection — YOLOv8 (HF Space)
This Hugging Face Space provides an interactive **YOLOv8 Small (YOLOv8s)** model for detecting and localizing defects in forged metal surfaces.
The model is trained on a custom industrial dataset containing **10 defect categories**, and can identify & localize defects directly from uploaded images.
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# 🧠 **About the Model**
This model comes from the repository:
➡️ **`code0ut/metal-defect-yolo`**
It was trained using:
- **YOLOv8s** (Ultralytics)
- **50 epochs**
- **640×640 image size**
- **AdamW optimizer (auto-selected)**
- **Google Colab T4 GPU**
The model was exported as `best.pt` and is automatically downloaded from the Hugging Face Hub when the Space loads.
---
# 🏷️ **Defect Classes (10 Total)**
The detector can identify the following metal surface defects:
1. punching_hole
2. welding_line
3. crescent_gap
4. water_spot
5. oil_spot
6. silk_spot
7. inclusion
8. rolled_pit
9. crease
10. waist_folding
These categories are common in industrial quality inspection systems used for forged or rolled metal products.
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# 🚀 **How to Use the Space**
Upload any metal surface image using the UI.
The model will:
- Run YOLOv8 inference
- Display bounding boxes
- Show class labels & confidence scores
- Return a processed output image
No code is needed — everything runs inside the browser.
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# 🧪 **Using the Model Programmatically (Python)**
You can also use the model directly in your Python environment:
```python
from ultralytics import YOLO
model = YOLO("code0ut/metal-defect-yolo")
results = model("image.jpg")
results.show() |