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---
license: mit
base_model:
- Ultralytics/YOLO11
tags:
- chemistry
---
# Molecule Detection YOLO in MolParser
From paper: "*MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild*" (ICCV2025 Accept)
[Arxiv Paper](https://arxiv.org/abs/2411.11098) | [Huggingface Dataset](https://huggingface.co/datasets/AI4Industry/MolParser-7M) | [OCSR Demo](https://ocsr.dp.tech/)
We provide several [ultralytics YOLO11]((https://github.com/ultralytics/ultralytics)) weights for molecule detection with different size & input resolution.
## 1⃣️ [MolDet-General] 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:
| Model Size | mAP50 | mAP50-95 | Speed (T4 TensorRT10) |
| ---- | ----- | -------- | ----- |
| n | 0.9581 | 0.8524 | 1.5 ± 0.0 ms |
| s | 0.9652 | 0.8704 | 2.5 ± 0.1 ms |
| m | 0.9686 | 0.8736 | 4.7 ± 0.1 ms |
| l | **0.9891** | **0.9028** | 6.2 ± 0.1 ms |
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)
```
## 2⃣️ [MolDet-Doc] 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
* prefer **single page PDF image** input
* better in small molecule detection
<span style='color:gray'>Warning: It is recommended to use MuPDF to render PDF pages at more than 144dpi.</span>
Result in private testing:
| Model Size | mAP50 | mAP50-95 | Speed (T4 TensorRT10) |
| ---- | ----- | -------- | ----- |
| n | 0.9871 | 0.8732 | 3.1 ± 0.0 ms |
| s | 0.9851 | 0.8824 | 5.5 ± 0.1 ms |
| m | 0.9867 | 0.8917 | 9.9 ± 0.2 ms |
| l | **0.9913** | **0.9011** | 13.1 ± 0.3 ms |
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)
```
## 📖 Citation
If you use this model in your work, please cite:
```
@article{fang2024molparser,
title={Molparser: End-to-end visual recognition of molecule structures in the wild},
author={Fang, Xi and Wang, Jiankun and Cai, Xiaochen and Chen, Shangqian and Yang, Shuwen and Tao, Haoyi and Wang, Nan and Yao, Lin and Zhang, Linfeng and Ke, Guolin},
journal={arXiv preprint arXiv:2411.11098},
year={2024}
}
``` |