---
tags:
- object-detection
- computer-vision
- yolo
- yolov11
- defect-detection
- industrial
- pytorch
library_name: ultralytics
license: apache-2.0
datasets:
- tile-defect-detection
language:
- en
metrics:
- map
base_model: ultralytics/yolo11l
pipeline_tag: object-detection
---

# 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](https://cdn-uploads.huggingface.co/production/uploads/65566a3b76fe5cfa6a104d18/Pw0qkXwdf4B0WI2yP82TS.jpeg)

## 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.

- **Developed by:** Can Deniz Kocak
- **Model type:** Object Detection (YOLO11)
- **Finetuned from model:** `yolo11l.pt` (YOLO11 Large)
- **License:** Apache 2.0

### Model Sources

- **Repository:** [huggingface.co/candenizkocak/tile-defect-detection-yolo11](https://huggingface.co/candenizkocak/tile-defect-detection-yolo11)
- **Framework:** [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)

## Uses

### Direct Use

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:
1.  **Edge defect** (ID: 0)
2.  **Corner defect** (ID: 1)
3.  **White spot** (ID: 2)
4.  **Light patch** (ID: 3)
5.  **Dark spot/patch** (ID: 4)
6.  **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
from ultralytics import YOLO
import cv2

# Load the model
model = YOLO("https://huggingface.co/candenizkocak/tile-defect-detection-yolo11/resolve/main/best.pt")

# Inference on a single image (or crop)
# Note: For large images, use a sliding window approach
results = model.predict("path/to/tile_crop.jpg", conf=0.35)

# Visualize
results[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.

### Software

- **Ultralytics:** 8.3.x
- **PyTorch:** 2.9.x
- **CUDA:** 12.x