README.md

metadata

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.

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🏷️ 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:

from ultralytics import YOLO

model = YOLO("code0ut/metal-defect-yolo")
results = model("image.jpg")
results.show()