Ashgibbs/Cosmetic_Defect_Detection

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:

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.