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:
CrazingInclusionPatchesPittedRolled-in ScaleScratches
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.
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# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Ashgibbs/Cosmetic_Defect_Detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True)