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---
license: cc-by-nc-sa-4.0
datasets:
- UniParser/MolDet-Bench
base_model:
- UniParser/MolDet
- Ultralytics/YOLO11
tags:
- chemistry
---
# Molecule Detection YOLO in MolParser2.0
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.
## [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](https://docs.ultralytics.com/modes/predict/).
## [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](https://docs.ultralytics.com/modes/predict/).
## 📊 BenchMark Results
Please refer to [MolDet-Bench](https://huggingface.co/datasets/UniParser/MolDet-Bench)
## 📜 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.
## 📖 Citation
If you use this model in your work, please cite:
```
Comming soon!
``` |