mcthebest/PCB_RTDETR

PCB Defect Detection — RT-DETRv4 X on DsPCBSD+

RT-DETRv4 X fine-tuned on the DsPCBSD+ dataset for 9-class copper-layer defect detection.

Live Demo

Try it in your browser without any setup:

https://huggingface.co/spaces/mcthebest/PCB_RTDETR

Upload your own PCB image or pick from built-in test images. The confidence threshold is adjustable in the sidebar.

Dataset: DsPCBSD+

DsPCBSD+ is a 2024 open dataset for PCB copper-layer defect detection, captured by a professional AOI system (AGLE'OL AOI-100 V8, 16K camera, controlled LED lighting). Nine defect categories, annotated at instance level:

Code	Defect	Description
SH	Short	Conductive bridge between two traces
SP	Spur	Anomalous copper spike from a trace
SC	Spurious Copper	Unwanted copper on board surface
OP	Open Circuit	Break in a conductive trace
MB	Mouse Bite	Edge notch or chip in the trace
HB	Hole Breakout	Damage to material around a drill hole
CS	Conductor Scratch	Scratch mark on a conductive trace
CFO	Conductor Foreign Object	Foreign particle on a trace
BMFO	Base Material Foreign Object	Foreign particle in substrate material

S. Lv et al., "A dataset for deep learning based detection of printed circuit board surface defect," Scientific Data, vol. 11, no. 1, p. 811, 2024. https://doi.org/10.1038/s41597-024-03656-8

Model

Architecture: RT-DETRv4 X

RT-DETRv4 builds on the RT-DETR lineage (v1, v2, v3) with ideas from D-FINE and DEIM. The main addition is a semantic distillation framework that uses a frozen DINOv3 teacher during training only, adding no cost at inference.

Component	Detail
Backbone	HGNetv2-X, multi-scale features at stride 8/16/32
Encoder	Efficient Hybrid Encoder: AIFI (global self-attention on S5) + CCFF (CNN cross-scale fusion)
Decoder	DFINETransformer, 6 layers, 300 queries, 32-bin probabilistic box regression per edge
Teacher (train only)	DINOv3 ViT-B/16 (frozen), trained on LVD-1689M (~1.7B images)
DSI module	Aligns F5 features with DINOv3 semantics via cosine similarity loss
GAM module	Adjusts DSI loss weight per epoch based on gradient norms

Training loss:

L_total = L_det + λ · L_DSI

L_det includes VFL, L1, GIoU, FGL, DDF, and MAL losses from D-FINE and DEIM. DINOv3 is not loaded at inference.

Training Configuration

Parameter	Value
Dataset	DsPCBSD+ (8,208 train / 2,051 val, 80-20 split)
Pretrained weights	RT-DETRv4-X COCO + DINOv3 ViT-B/16 (LVD-142M)
Input resolution	640 x 640 px
Epochs	10 (72 recommended; limited by Colab T4)
Batch size	8
Optimizer	AdamW (lr encoder/decoder = 2e-4, lr backbone = 1e-5, wd = 1e-4)
LR Scheduler	FlatCosine (warmup 500 iter, flat 5 epoch, no-aug 2 epoch)
Mixed precision	AMP FP16/FP32
Hardware	NVIDIA Tesla T4 (16 GB VRAM), Google Colab

Results on DsPCBSD+ Validation Set

Qualitative Examples

Ground truth vs. predictions at conf≥0.3. The model correctly localises all four defects in the first example (BMFO×2, MB, CFO) and all three Mouse Bite instances in the second, with one low-confidence extra detection.

Overall COCO Metrics

Metric	Value
mAP @ IoU=0.50	0.863
mAP @ IoU=0.50:0.95	0.522
mAP @ IoU=0.75	0.551
AP small (<32² px)	0.439
AP medium	0.602
AP large (>96² px)	0.754
AR @ maxDets=100	0.686

Per-Class AP @ IoU=0.50:0.95

Class	Full Name	AP
SH	Short Circuit	0.597
SP	Spur (Copper Spike)	0.381
SC	Spurious Copper	0.511
OP	Open Circuit	0.555
MB	Mouse Bite	0.408
HB	Hole Breakout	0.807
CS	Conductor Scratch	0.504
CFO	Conductor Foreign Object	0.462
BMFO	Base Material Foreign Object	0.478
Mean		0.522

Per-Class F1 @ Conf≥0.5, IoU≥0.5

Class	Precision	Recall	F1
SH	0.91	0.86	0.88
SP	0.77	0.69	0.73
SC	0.89	0.77	0.83
OP	0.85	0.84	0.84
MB	0.87	0.69	0.77
HB	0.91	0.93	0.92
CS	0.82	0.65	0.73
CFO	0.86	0.56	0.68
BMFO	0.87	0.83	0.85
Mean			0.802

Confusion Matrix @ Conf≥0.5, IoU≥0.5

Raw counts (left) and row-normalised rates (right). HB is the strongest class (0.93 recall); CFO and CS have the most false negatives, largely absorbed into Background.

Comparison with SOTA on DsPCBSD+

Model	Backbone	mAP@50	mAP@50:95	Notes
YOLOv11-CGL	YOLOv11n	84.5%	51.6%	300 epochs, lightweight
PCB-AM	YOLOv8s	85.7%	N/A	Attention-guided modules
PCB-FS	YOLOv8	86.2%	52.4%	Frequency-spatial features
RT-DETRv4 X (ours)	HGNetv2-X + DINOv3	86.3%	52.25%	10 epochs, T4 only

RT-DETRv4 X matches or exceeds all compared methods at only 10 epochs on a single T4. PCB-FS, for comparison, used 100+ epochs with more resources.

Running Locally

Clone the Space and install dependencies:

git clone https://huggingface.co/spaces/mcthebest/PCB_RTDETR
cd PCB_RTDETR
pip install -r requirements.txt

Download the checkpoint:

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="mcthebest/PCB_RTDETR",
    repo_type="model",
    filename="last.pth",
)

Or place it manually at outputs/rtv4_hgnetv2_x_pcb/last.pth, then run:

streamlit run app.py

How Inference Works

The image is resized to 640×640 and passed through RT-DETRv4 X. The decoder outputs 300 candidate queries which the postprocessor filters by confidence threshold (default 0.30). Detections are returned as (labels, boxes, scores) and drawn with labeled bounding boxes. DINOv3 is not loaded at inference.

Known Limitations

• Small defects (AP=0.439): sub-32px defects like SP (Spur) are the hardest class.
• Only 10 epochs were run vs. the recommended 72. The learning curve had not plateaued, so more training should meaningfully improve mAP@50:95.
• The dataset was collected under controlled AOI conditions. Images from different lighting or camera setups may need domain adaptation.
• AR@100=0.686, meaning roughly 31% of defects are missed. Not production-ready for zero-miss QC pipelines.

References

• RT-DETRv4 — Liao et al., 2025
• D-FINE — Peng et al., ICLR 2025
• DEIM — Huang et al., CVPR 2025
• DINOv3 — Siméoni et al., 2025
• RT-DETRv4 GitHub
• DsPCBSD+ dataset