Model Card for Industrial Tile Defect Detection (YOLO11-Large)
This model is a fine-tuned version of YOLO11-Large designed to detect minute manufacturing defects on industrial tiles. It was trained using a sliding-window tiling approach with aggressive oversampling to handle severe class imbalance.
Ceramic Tile Defect Detection
Model Details
Model Description
This model solves the problem of detecting tiny defects (approx. 10-50 pixels) on massive industrial images (8192x6000 pixels). Standard resizing destroys these defects, so this model was trained on 640x640 crops generated from the original high-resolution data.
To address the rarity of specific defects (like "Halos"), the training pipeline utilized a dynamic oversampling strategy where rare defect crops were physically duplicated with random jitter, ensuring the model saw a balanced distribution of classes.
The model is intended for Industrial Quality Control (QC) systems. It accepts 640x640 image tensors.
Important: Because the original images are high-resolution (4K/8K), this model should be used within a Sliding Window Inference pipeline. Running it directly on a downscaled 8000px image will result in poor performance.
Classes Detected
The model detects 6 specific defect categories:
Edge defect (ID: 0)
Corner defect (ID: 1)
White spot (ID: 2)
Light patch (ID: 3)
Dark spot/patch (ID: 4)
Halo (ID: 5)
Out-of-Scope Use
This model is not suitable for general object detection (people, cars, etc.).
It is optimized for gray/textured industrial tile backgrounds. Using it on different materials (wood, metal) may require fine-tuning.
How to Get Started with the Model
You can use this model directly with the ultralytics library.
python
1from ultralytics import YOLO
2import cv2
34# Load the model5model = YOLO("https://huggingface.co/candenizkocak/tile-defect-detection-yolo11/resolve/main/best.pt")67# Inference on a single image (or crop)8# Note: For large images, use a sliding window approach9results = model.predict("path/to/tile_crop.jpg", conf=0.35)1011# Visualize12results[0].show()
Training Details
Training Data
The dataset consists of 5,388 high-resolution industrial images.
Preprocessing: Images were sliced into 640x640 tiles.
Augmentation: Random crop jittering was applied to duplicate defects.
Training Procedure
To combat the massive class imbalance (e.g., ~9000 Dark Spots vs ~300 Halos), an Aggressive Oversampling strategy was applied during the tiling phase:
Class
Oversample Ratio
Dark Spot
1x (No change)
Light Patch
8x
White Spot
4x
Corner Defect
4x
Edge Defect
15x
Halo
27x
This ensured that the model saw approximately ~8,000 examples of each class during training.
Training Hyperparameters
Epochs: 50
Batch Size: 32
Image Size: 640
Optimizer: Auto (AdamW)
Hardware: NVIDIA A100 (80GB)
Evaluation
Metrics
The model achieves strong performance across all classes, with particularly notable success in detecting the rare "Halo" class due to the oversampling strategy.
mAP@50 (Mean Average Precision): ~78%
Recall (Dark Spots): ~88%
Recall (Halos): ~74% (Significantly improved from baseline)
Recommendations
False Positives on Background:
The model may occasionally detect defects on the black background (conveyor belt). It is highly recommended to use a Region of Interest (ROI) filter (such as convex hull detection on the tile) to ignore predictions outside the tile area.
Environmental Impact
Hardware Type: NVIDIA A100-SXM4-80GB
Hours used: ~2 hours
Cloud Provider: Google Colab Pro+
Compute Region: US-Central1
Technical Specifications
Model Architecture
YOLO11 employs a CSP-based backbone with an improved detect head compared to YOLOv8, offering a better trade-off between latency and accuracy for small object detection.
