---
license: mit
tags:
- object-detection
- pcb-defect-detection
- yolov8
- industrial-inspection
- domain-generalization
- gerber
- automated-optical-inspection
datasets:
- norbertelter/pcb-defect-dataset
metrics:
- map
library_name: ultralytics
pipeline_tag: object-detection
---

# GerberFormer: Design-Conditioned PCB Defect Detection

## TL;DR

Image-only PCB defect detectors fail catastrophically on unseen board families (mAP@50: **0.06**).
Adding a synthetic Gerber prior closes this gap completely (mAP@50: **0.99**) with zero inference overhead.

## Model Description

GerberFormer addresses a fundamental weakness in automated optical inspection (AOI):
standard detectors memorize the visual appearance of specific PCB board families rather
than learning what defects fundamentally are. When deployed on a new board family never
seen during training, performance collapses.

Our solution: blend a synthetic design prior (derived from median board images using
CLAHE enhancement and adaptive thresholding) into the input image at a controlled
strength alpha=0.25. This gives the model structural context about what the board
should look like, enabling it to detect deviations regardless of board identity.

## Key Results

| Model | Seen Boards mAP@50 | Unseen Boards mAP@50 |
|-------|--------------------|----------------------|
| YOLOv8s image-only (baseline) | 0.9924 | 0.0588 |
| YOLOv8s + Gerber alpha=0.10 | 0.9902 | 0.9890 |
| **YOLOv8s + Gerber alpha=0.25 (ours)** | **0.9916** | **0.9928** |
| YOLOv8s + Gerber alpha=0.50 | 0.9817 | 0.9821 |

The generalization gap closes from 0.934 to 0.001.

## Per-Class Performance (Test Set)

| Defect Class | Baseline AP@50 | Gerber AP@50 |
|--------------|----------------|--------------|
| missing_hole | 0.9943 | 0.9937 |
| mouse_bite | 0.9949 | 0.9949 |
| open_circuit | 0.9941 | 0.9937 |
| short | 0.9923 | 0.9901 |
| spurious_copper | 0.9950 | 0.9950 |
| spur | 0.9871 | 0.9908 |

## Inference Speed

No overhead from Gerber conditioning.

| Model | Preprocess | Inference | Total |
|-------|-----------|-----------|-------|
| Baseline | 0.96ms | 1.44ms | 3.39ms |
| Gerber (ours) | 0.79ms | 1.26ms | 2.87ms |

## Models in this Repository

| File | Description | Val mAP@50 |
|------|-------------|------------|
| `yolov8_baseline.pt` | YOLOv8s image-only baseline, 100 epochs | 0.992 |
| `yolov8_gerber_alpha025.pt` | Main model, Gerber fusion alpha=0.25, 100 epochs | 0.992 |
| `yolov8_gerber_alpha010.pt` | Ablation, Gerber fusion alpha=0.10, 50 epochs | 0.992 |
| `yolov8_gerber_alpha050.pt` | Ablation, Gerber fusion alpha=0.50, 50 epochs | 0.981 |

## Usage

### Quick Start
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

path = hf_hub_download(
    repo_id  = "pulipakav-1/gerberformer",
    filename = "yolov8_gerber_alpha025.pt"
)
model   = YOLO(path)
results = model.predict("your_pcb_image.jpg", conf=0.3)
results[0].show()
```

### With Gerber Prior (recommended for unseen boards)
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
import cv2, numpy as np

model_path = hf_hub_download(
    repo_id  = "pulipakav-1/gerberformer",
    filename = "yolov8_gerber_alpha025.pt"
)

# Load your board image and prior
img   = cv2.imread("your_pcb_image.jpg")
img   = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img   = cv2.resize(img, (640, 640))
prior = cv2.imread("your_board_prior.png")
prior = cv2.cvtColor(prior, cv2.COLOR_BGR2RGB)
prior = cv2.resize(prior, (640, 640))

# Blend with alpha=0.25
fused = cv2.addWeighted(
    img.astype(np.float32),   0.75,
    prior.astype(np.float32), 0.25, 0
).astype(np.uint8)
cv2.imwrite("fused_input.jpg", cv2.cvtColor(fused, cv2.COLOR_RGB2BGR))

# Inference
model   = YOLO(model_path)
results = model.predict("fused_input.jpg", conf=0.3)
results[0].show()
```

### Build Your Own Gerber Prior
```python
import cv2, numpy as np
from pathlib import Path

def build_gerber_prior(image_dir, n_samples=100, img_size=640):
    images = sorted(Path(image_dir).glob("*.jpg"))[:n_samples]
    stack  = []
    for p in images:
        img = cv2.imread(str(p))
        img = cv2.resize(img, (img_size, img_size))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        stack.append(img.astype(np.float32))
    median   = np.median(np.stack(stack), axis=0).astype(np.uint8)
    gray     = cv2.cvtColor(median, cv2.COLOR_RGB2GRAY)
    clahe    = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    enhanced = clahe.apply(gray)
    adaptive = cv2.adaptiveThreshold(
        enhanced, 255,
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY, 21, 5)
    sobelx   = cv2.Sobel(enhanced, cv2.CV_64F, 1, 0, ksize=3)
    sobely   = cv2.Sobel(enhanced, cv2.CV_64F, 0, 1, ksize=3)
    gradient = np.sqrt(sobelx**2 + sobely**2)
    gradient = cv2.normalize(gradient, None, 0, 255,
                              cv2.NORM_MINMAX).astype(np.uint8)
    return np.stack([enhanced, adaptive, gradient], axis=-1)

prior = build_gerber_prior("path/to/your_board_images/")
cv2.imwrite("my_board_prior.png", prior)
```

## Dataset

PKU PCB Defect Dataset
- 10,664 images (8,534 train / 1,066 val / 1,068 test)
- 6 defect classes: missing_hole, mouse_bite, open_circuit, short, spurious_copper, spur
- 13 board families: l_light, light_01-12, rotation_90, rotation_270
- Download: https://www.kaggle.com/datasets/norbertelter/pcb-defect-dataset

## Reproduce from Scratch
```bash
git clone https://github.com/pulipakav-1/gerberformer
cd gerberformer
pip install -r requirements.txt

kaggle datasets download -d norbertelter/pcb-defect-dataset
unzip pcb-defect-dataset.zip -d pcb-defect-dataset

python priors/build_priors.py
python priors/build_fused_dataset.py
python training/train_gerber.py

python evaluation/evaluate_per_family.py \
    --model experiments/gerber_alpha025/weights/best.pt \
    --imgdir pcb-fused-dataset/test/images \
    --lbldir pcb-defect-dataset/test/labels
```

## Limitations

1. Requires multiple training images from the same board family to build a reliable prior
2. Uses appearance-based priors rather than true Gerber CAD files
3. Alpha blending is a simple fusion — learned fusion could do better
4. Prior quality degrades for board families with very few training images

## Future Work

- Replace synthetic priors with real Gerber file rendering
- Implement learned cross-modal fusion via cross-attention
- Extend to multi-layer PCB inspection
- Deploy with ONNX/TensorRT for real-time AOI

## Links

- Code: https://github.com/pulipakav-1/gerberformer
- Results and figures: https://huggingface.co/datasets/pulipakav-1/gerberformer-results

## Citation
```bibtex
@article{gerberformer2025,
  title={Design-Conditioned PCB Defect Detection via Synthetic Gerber Priors},
  author={Venkata Naga Sai Vishnu Rohit Pulipaka},
  journal={Under Review},
  year={2025}
}
```

## License

MIT