dimtri009/rfdetr-pid-detector

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RF-DETR P&ID Symbol Detector

A fine-tuned RF-DETR model for detecting Process & Instrumentation Diagram (P&ID) symbols.

The model was developed as part of the paper:

Towards Automated P&ID Digitization: Graph-Based OCR Consolidation and Global Symbol–Tag Association
Accepted at ACM Symposium on Document Engineering (DocEng 2026).

The detector recognizes the graphical symbols appearing in industrial P&IDs and is intended as the first stage of a complete P&ID digitization pipeline.

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Training

The model was fine-tuned for 10 epochs on a custom P&ID symbol dataset containing 32 symbol classes. During training, both the base model and its Exponential Moving Average (EMA) weights were monitored. The EMA model was selected as the final checkpoint because it consistently achieved higher detection performance.

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Detected Classes

The model detects the following classes:

ID	Class
0	Not_used
1	Gate_Valve
2	Ball_Valve
3	Globe_valve_NO
4	Gate_valve_NO
5	Globe_valve_NO
6	Butterfly_valve
7	Plug_valve
8	Check_valve
9	Diaphragm_valve
10	Needle_valve
11	Half_Filled_Gate_Valve
12	Gate_Valve_NC
13	Globle_valve_NC
14	Control_Valve
15	Rotary_Valve
16	Ball_valve_NC
17	Paddle_blind
18	Spectacle_blind_Closed
19	Spectacle_blind_Open
20	Reducer
21	Flange_or_Nozzle
22	Rupture_disk
23	Pipe_Insulation_or_Tracing
24	Flow_Arrow
25	Sight_glass
26	Instrument_Field
27	Instrument_Field
28	Instrument_Panel
29	Instrument_Aux_Panel
30	Box
31	Instrument_Panel
32	Box

---

Installation

pip install rfdetr supervision

For tiled inference:

pip install sahi

---

Load the model

from rfdetr import RFDETRBase

model = RFDETRBase(
    pretrain_weights="checkpoint_best_total.pth"
)

---

Inference (without SAHI)

import cv2
import supervision as sv

image = cv2.imread("image.png")

detections = model.predict(
    image,
    threshold=0.5
)

labels = [
    f"{CLASS_NAMES[c]} {conf:.2f}"
    for c, conf in zip(
        detections.class_id,
        detections.confidence
    )
]

annotated = image.copy()

annotated = sv.BoxAnnotator().annotate(
    annotated,
    detections
)

annotated = sv.LabelAnnotator().annotate(
    annotated,
    detections,
    labels
)

sv.plot_image(annotated)

---

Inference using SAHI

For very large engineering drawings (typically PDF pages rendered at high resolution), tiled inference significantly improves recall.

Recommended parameters:

• Slice size: 1280 × 1280
• Overlap: 20%

from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction

detection_model = AutoDetectionModel.from_pretrained(
    model_type="roboflow",
    model=model,
    confidence_threshold=0.5,
    category_mapping=CLASS_NAMES,
    device="cuda",
)

result = get_sliced_prediction(
    image,
    detection_model=detection_model,
    slice_height=1280,
    slice_width=1280,
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2,
)

The resulting detections are available in

result.object_prediction_list

or can be converted into Supervision detections for visualization.

---

Example

Without SAHI:

Large drawings may miss small symbols.

With SAHI:

Large drawings are processed tile-by-tile, improving the detection of small symbols and densely packed regions.

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📊 Test Performance

Overall Performance

Metric	Score
mAP@0.50:0.95	97.89%
mAP@0.50	99.96%
Precision	99.97%
Recall	99.00%

---

Per-Class Performance

Class	mAP@50:95	mAP@50	Precision	Recall
Gate_Valve	0.9906	1.0000	1.0000	0.99
Ball_Valve	0.9908	0.9999	1.0000	0.99
Globe_valve_NO	0.9904	1.0000	1.0000	0.99
Gate_valve_NO	0.9896	1.0000	1.0000	0.99
Butterfly_valve	0.9751	1.0000	1.0000	0.99
Plug valve	0.9775	1.0000	1.0000	0.99
Check_valve	0.9805	1.0000	1.0000	0.99
Diaphragm_valve	0.9812	1.0000	1.0000	0.99
Needle_valve	0.9950	1.0000	1.0000	0.99
Half_Filled_Gate_Valve	0.9915	1.0000	1.0000	0.99
Gate_Valve_NC	0.9881	1.0000	1.0000	0.99
Globle_valve_NC	0.9913	1.0000	1.0000	0.99
Control_Valve	1.0000	1.0000	1.0000	0.99
Rotary_Valve	0.9519	1.0000	1.0000	0.99
Ball_valve_NC	0.9608	1.0000	1.0000	0.99
Paddle_blind	0.9606	1.0000	1.0000	0.99
Spectacle_blind_Closed	0.9627	1.0000	1.0000	0.99
Spectacle_blind_Open	0.9651	0.9999	1.0000	0.99
Reducer	0.9864	1.0000	1.0000	0.99
Flange_or_Nozzle	0.9445	0.9901	1.0000	0.99
Rupture_disk	0.9843	0.9997	1.0000	0.99
Pipe_Insulation_or_Tracing	0.9864	1.0000	1.0000	0.99
Flow_Arrow	0.9447	1.0000	1.0000	0.99
sight_glass	0.9901	1.0000	1.0000	0.99
Instrument_Field	0.9881	0.9998	0.9982	0.99
Instrument_Panel	0.9890	0.9999	0.9947	0.99
Instrument_Aux_Panel	0.9875	0.9999	0.9982	0.99
Box	0.9482	1.0000	0.9981	0.99

---

🚀 Highlights

• 33 P&ID symbol classes
• mAP@50: 99.96%
• mAP@50:95: 97.89%
• Precision: 99.97%
• Recall: 99.00%
• Control Valve achieved perfect detection performance (100% mAP).
• More than 85% of the classes achieved a mAP@50:95 greater than 98%.
• The EMA model consistently outperformed the base model during training and was selected as the final released checkpoint.
• Optimized for high-resolution P&ID drawings and compatible with SAHI for sliced inference on large engineering diagrams.

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Citation

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

@inproceedings{XXXX,
  title={Towards Automated P\&ID Digitization: Graph-Based OCR Consolidation and Global Symbol--Tag Association},
  author={...},
  booktitle={Proceedings of the ACM Symposium on Document Engineering (DocEng)},
  year={2026}
}

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Acknowledgements

This model is built upon the excellent RF-DETR object detector and supports tiled inference through SAHI.

• RF-DETR: https://github.com/roboflow/rf-detr
• SAHI: https://github.com/obss/sahi

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License

Please refer to the license accompanying this repository.