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license: apache-2.0
library_name: structflo-cser
tags:
- object-detection
- chemistry
- document-analysis
---
# cser-detector — structflo-cser weights
2-class page detector (`chemical_structure`, `compound_label`) for chemistry documents. Architecture: **D-FINE-L** (HGNet-V2 backbone, `transformers` `DFineForObjectDetection`, Apache-2.0), initialised from `ustc-community/dfine-large-coco` (Apache-2.0, COCO-only pretraining) and trained clean-room on synthetic pages rendered with RDKit from ChEMBL SMILES, then fine-tuned on an internal annotated corpus. Single-file `.safetensors` checkpoint with the model config in its metadata; load with `structflo.cser.inference.dfine.DFineDetector.from_file`.
Versions v0.1–v0.4 of this repo were Ultralytics YOLO11l checkpoints and are retired (AGPL-3.0 lineage); use v1.0 or later with `structflo-cser` >= 1.0.
| field | value |
|---|---|
| latest version | `v1.0` (HF tag `weights-v1.0`) |
| file | `best.safetensors` |
| sha256 | `29962b7115159d93b6b6d3f16b96769a6ffc34e3590e6697687a8e3425ad0bc7` |
| requires | `structflo-cser>=1.0.0,<2.0.0` |
## Usage
```python
from structflo.cser.pipeline import ChemPipeline
pipeline = ChemPipeline() # resolves the latest registered weights
pairs = pipeline.process("page.png")
```
Pin a version with `ChemPipeline(weights="v1.0")`.
## Licence and provenance
Weights: Apache-2.0. Training data: synthetic pages rendered with RDKit from ChEMBL
(CC-BY-SA-3.0) SMILES plus an internal annotated corpus (not released). See the package's
`THIRD_PARTY_NOTICES.md` for upstream attributions.
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