molparser-visual-ocsr

name	molparser-visual-ocsr
description	Use for MolParser visual OCSR workflows that parse molecule images or PDFs into E-SMILES/SMILES with MolDet detection and MolParser recognition models.

MolParser Visual OCSR Skill

Use this skill when extracting molecular structures from images, PDFs, URLs, or mixed input lists with molparser.MolParser.

Workflow

1. Use molparser/models/config.yaml defaults or pass a custom YAML path to MolParser.
2. Prefer device="auto" unless CPU/GPU must be forced.
3. For PIL images, image paths, or image URLs:
   • Use rec_only=True when the image is already a single molecule crop.
   • Use rec_only=False when detection is needed; this uses moldet_v2_yolo11n_640_general.pt.
4. For PDF paths or PDF URLs:
   • rec_only is ignored; render pages with pypdfium2 at pdf_dpi=300.
   • Detect molecules with moldet_v2_yolo11n_960_doc.pt.
5. Recognize crops with the MolParser recognition model, then normalize with postprocess_caption.
6. Use batch defaults unless memory requires tuning: MolDet batch size is 8, MolParser OCSR batch size is 16.
7. Multi-image inputs are batched together; PDF page detections are cropped first, then molecule crops are batched together for recognition.
8. Use moldet.expand_px for detector box expansion and molparser.padding_px for recognition input padding; defaults are expand_px=2 and padding_px=0, and both can be overridden in parse(...).

Minimal Example

from molparser import MolParser

parser = MolParser(device="auto", pdf_dpi=300)

results = parser.parse(["mol.png", "paper.pdf"], rec_only=False)
for item in results:
    print(item.input_index, item.page_index, item.bbox, item.esmi)

For high-throughput molecule crops:

parser = MolParser(device="cuda", molparser_batch_size=1024)
results = parser.parse(["mol_001.png", "mol_002.png"], rec_only=True)

Model Download Policy

• Local paths win when configured.
• Hugging Face is tried first when the HF model ID is set.
• If Hugging Face fails and the ModelScope ID is set, automatically fall back to ModelScope.
• Default MolDet files are moldet_v2_yolo11n_640_general.pt for image detection and moldet_v2_yolo11n_960_doc.pt for PDF page detection.
• Use environment variables for proxy and private tokens:

export HTTP_PROXY=100.68.165.249:3128
export HTTPS_PROXY=100.68.165.249:3128
export HF_TOKEN=hf_xxx

Output Fields

Each result includes source, input_index, page_index, bbox, confidence, raw_caption, caption, smi, esmi, cxsmiles, markush, sru, and groups.

• All parse methods return list[MolParserResult].
• Single image with rec_only=True: one whole-image result; bbox, confidence, and page_index are None.
• Image with rec_only=False: one result per detected molecule; no detections returns an empty list.
• PDF input: one result per detected molecule on rendered PDF pages; page_index, bbox, and confidence are populated.
• List input: flat result list; use input_index to map each result back to the original list item.

License Notes

MolDetv2 weights are non-commercial according to their model card. The PyTorch detection path uses Ultralytics YOLO; closed-source, private, SaaS, internal business, or commercial usage may require AGPL-3.0 compliance or an Ultralytics Enterprise license.