---
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.

![image](https://cdn-uploads.huggingface.co/production/uploads/65f7f16fb6941db5c2e7c4bf/iZqZ8rUsD6jacIJr8Hbag.png)

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.

![image](https://cdn-uploads.huggingface.co/production/uploads/65f7f16fb6941db5c2e7c4bf/rKZjaZ0EingRtxdIe5Ptz.png)

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!
```