---
license: mit
---

# Molecule Detection YOLO in MolParser

From paper: "*MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild*" (ICCV2025 under review)

We provide several [ultralytics YOLO11]((https://github.com/ultralytics/ultralytics)) weights for molecule detection with different size & input resolution.

## general molecule structure detection models

`moldet_yolo11[size]_640_general.pt`

YOLO11 weights trained on 35k human annotated image crops and 100k generated images

* 640x640 input resolution
* support handwritten molecules
* multiscale input (inputs can be single/multiple molecular cutouts, reaction or table cutouts, or single-page PDF images)

<span style='color:gray'>Warning: For single-molecule input (used as a classification model), appropriate padding can be added to enhance the performance.</span>

Result in private testing:
| size | map50 | map50-95 |
| ---- | ----- | -------- |
| n | 0.9581 | 0.8524 |
| s | 0.9652 | 0.8704 |
| m | 0.9686 | 0.8736 |
| l | 0.9891 | 0.9028 |

usage:
```python
from ultralytics import YOLO
model = YOLO("moldet_yolo11l_640_general.pt")
model.predict("path/to/image.png", save=True, imgsz=640, conf=0.5)
```

## PDF molecule structure detection models

`moldet_yolo11[size]_960_doc.pt`

YOLO11 weights trained on 26k human annotated PDF pages (patents, papers, and books)

* 960x960 input resolution
* single page PDF image input

<span style='color:gray'>Warning: It is recommended to use MuPDF to render PDF pages at more than 144dpi.</span>


Result in private testing:
| size | map50 | map50-95 |
| ---- | ----- | -------- |
| n | 0.9871 | 0.8732 |
| s | 0.9851 | 0.8824 |
| m | 0.9867 | 0.8917 |
| l | 0.9913 | 0.9011 |

usage:
```python
from ultralytics import YOLO
model = YOLO("moldet_yolo11l_960_doc.pt")
model.predict("path/to/pdf_page_image.png", save=True, imgsz=960, conf=0.5)
```