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UniParser/
MolDetv2

onnx

chemistry

UniParser/MolDet-Bench

Ultralytics/YOLO11

quantized

cc-by-nc-sa-4.0

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MolDetv2: A Smaller, Faster, and More Powerful Molecular Detection Model

Compared to MolDet, our new MolDetv2 model leverages more manually annotated training data, with further optimizations specifically for reducing molecular false detections and improving bounding box regression, achieving stronger performance with a smaller model.

[MolDet-General] universal molecule structure detection

YOLO11-n weights trained on more than 100k human annotated image crops & synthesis molecule images.

features:

640x640 input resolution
support handwritten molecules detection
multiscale input (inputs can be single/multiple molecular cutouts, reaction or table cutouts, or single-page PDF images)
update: MolDetv2 substantially reduces false positives on formulas, ball-and-stick diagrams, etc.

usage:

python

from ultralytics import YOLO
model = YOLO("/path/to/moldet_v2_yolo11n_640_general.pt") # for cpu only inference: using `moldet_v2_yolo11n_640_general.onnx` for faster speed
model.predict("path/to/image.png", save=True, imgsz=640, conf=0.5)

For further usage instructions, please refer to the official Ultralytics documentation.

[MolDet-Doc] document molecule structure detection

YOLO11-n weights trained on more than 60k human annotated PDF pages (patents, papers, and books) and 10k synthesis PDF pages with molecule images.

features:

960x960 input resolution
prefer single page PDF image input
better in small molecule detection
update: MolDetv2 substantially reduces false positives on formulas, ball-and-stick diagrams, and graphical symbols, with tighter bounding box alignment to molecular edges.

usage:

python

from ultralytics import YOLO
import fitz # MuPDF
pdf = fitz.open("doc.pdf")
model = YOLO("/path/to/moldet_v2_yolo11n_960_doc.pt")  # for cpu only inference: using `moldet_v2_yolo11n_960_doc.onnx` for faster speed
bboxes = []
for i, p in enumerate(pdf):
    img = f"page_{i}.png"; p.get_pixmap().save(img)
    for r in model.predict(img, imgsz=960, conf=0.5):
        for box in r.boxes:
            bboxes.append({"page":img, "conf":float(box.conf), "bbox":box.xyxy[0].tolist()})

For further usage instructions, please refer to the official Ultralytics documentation.

📊 BenchMark Results

Please refer to MolDet-Bench

🔍 Demo

MolDetv2 Gradio Demo

📜 License

MolDet & MolDetv2 model weights are provided for non-commercial use only.

For commercial use, please contact: fangxi@dp.tech or add a discussion in HuggingFace.

📖 Citation

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

Comming soon!

UniParser/MolDetv2