---
title: MolDetv2
canonical_url: "https://www.modelscope.cn/models/UniParser/MolDetv2"
md_url: "https://www.modelscope.cn/models/UniParser/MolDetv2.md"
repository: UniParser/MolDetv2
last_updated: 2026-09-09
license: cc-by-nc-sa-4.0
base_model:
  - UniParser/MolDet
  - Ultralytics/YOLO11
base_model_relation: finetune
library_name:
  - onnx
  - pytorch
frameworks:
  - pytorch
downloads: 49
stars: 0
tags:
  - chemistry
---

# MolDetv2

> MolDetv2 - UniParser 在 ModelScope 开源的模型。MolDetv2: A Smaller, Faster, and More Powerful Molecular Detection Model

UniParser/MolDetv2 是 ModelScope 魔搭社区上的机器学习模型，采用 cc-by-nc-sa-4.0 许可，基于 UniParser/MolDet、Ultralytics/YOLO11 构建。

- **Repository**: UniParser/MolDetv2
- **License**: cc-by-nc-sa-4.0
- **Base model**: UniParser/MolDet, Ultralytics/YOLO11
- **Tags**: chemistry
- **Downloads**: 49
- **Stars**: 0
- **Last updated**: 2026-09-09

Source: https://www.modelscope.cn/models/UniParser/MolDetv2

---

# 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.


## [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)


## 🔍 Demo

[MolDetv2 Gradio Demo](https://huggingface.co/spaces/AI4Industry/MolDet)


## 📜 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:

```
@article{fang2026molparserm,
  title={MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining},
  author={Fang, Xi and Lu, Haocheng and Lyu, Han and Luo, Chengxiang and Zhang, Linfeng and Ke, Guolin},
  journal={arXiv preprint arXiv:2609.05807},
  year={2026}
}
```