NoBSPCB

--- 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} } ```

Source context: 2 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo oborxel/noBSPCB