| --- |
| license: cc-by-nc-sa-4.0 |
| datasets: |
| - UniParser/MolDet-Bench |
| base_model: |
| - UniParser/MolDet |
| - Ultralytics/YOLO11 |
| tags: |
| - chemistry |
| --- |
| |
|
|
| # MolDetv2: A Smaller, Faster, and More Powerful Molecular Detection Model |
|
|
| Compared to [MolDet](https://huggingface.co/UniParser/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. |
|
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|
|
| ## [MolDet-General] universal molecule structure detection |
|
|
| YOLO11-n weights trained on more than 100k human annotated image crops & synthesis molecule images. |
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|  |
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| 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](https://docs.ultralytics.com/modes/predict/). |
|
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|
|
| ## [MolDet-Doc] document molecule structure detection |
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| YOLO11-n weights trained on more than 60k human annotated PDF pages (patents, papers, and books) and 10k synthesis PDF pages with molecule images. |
|
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|  |
|
|
| 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](https://docs.ultralytics.com/modes/predict/). |
|
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|
|
| ## 📊 BenchMark Results |
|
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| Please refer to [MolDet-Bench](https://huggingface.co/datasets/UniParser/MolDet-Bench) |
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|
|
| ## 🔍 Demo |
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| [MolDetv2 Gradio Demo](https://huggingface.co/spaces/AI4Industry/MolDet) |
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|
|
| ## 📜 License |
|
|
| MolDet & MolDetv2 model weights are provided for **non-commercial use only**. |
|
|
| For commercial use, please contact: [fangxi@dp.tech](mailto:fangxi@dp.tech) or add a discussion in HuggingFace. |
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|
|
| ## 📖 Citation |
|
|
| If you use this model in your work, please cite: |
|
|
| ``` |
| Comming soon! |
| ``` |
