README.md

---
license: mit
library_name: ultralytics
pipeline_tag: object-detection
tags:
- pcb
- defect-detection
- yolov8
- mc-dropout
- uncertainty-quantification
---

# noBSPCB: PCB Defect Detection with Monte Carlo Dropout

## Model Description

This model is a modified YOLOv8n architecture with an additional Dropout layer (p=0.1) placed before the Detect head. It is designed for detecting 6 types of PCB defects:
- missing_hole
- mouse_bite
- open_circuit
- short
- spur
- spurious_copper

The model supports Monte Carlo Dropout (MCD) inference: running multiple forward passes with Dropout enabled to estimate epistemic uncertainty. The variance of confidence scores across passes indicates prediction reliability.

## Training Dataset

- **Source:** PCB Defect dataset (Norbert Elter, Peking University)
- **Size:** 10,668 images
- **Split:** train/val/test (8534/1066/1068)

## Key Results

| Metric | Value |
|--------|-------|
| mAP@0.5 | 0.978 |
| False Positives (on test set) | 30 |
| Recall | 0.987 |
| Inference time (CPU) | 31 ms (baseline) / 628 ms (MCD) |

The hybrid pipeline achieves 91.5% false positive reduction compared to baseline YOLOv8n.

## Usage

### Standard Inference

```python
from ultralytics import YOLO

model = YOLO("oborxel/noBSPCB")
results = model("path/to/pcb_image.jpg")
```
### Monte Carlo Dropout Inference

For uncertainty estimation, multiple passes are required:

```python
from ultralytics import YOLO
import torch
import numpy as np

def enable_dropout(model):
    for m in model.model.modules():
        if isinstance(m, torch.nn.Dropout):
            m.train()

model = YOLO("oborxel/noBSPCB")
model.model.eval()
enable_dropout(model.model)

num_passes = 30
all_confs = []

for _ in range(num_passes):
    results = model("image.jpg", verbose=False)
    if results[0].boxes is not None:
        confs = results[0].boxes.conf.cpu().numpy()
        all_confs.extend(confs)

variance = np.var(all_confs) if all_confs else 0.0
print(f"Uncertainty (variance): {variance:.4f}")
print(f"Verdict: {'defect' if variance < 0.02 else 'uncertain'}")
```

## License

MIT

## Citation

If you use this model in your work, please cite:

```
@software{noBSPCB,
  author = {Chukhlov, Alexander},
  title = {noBSPCB: PCB Defect Detection with Monte Carlo Dropout},
  year = {2026},
  url = {https://github.com/ex-alander/noBSPCB}
}
```