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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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tags: |
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- text-detection |
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- ocr |
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- dbnet |
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- repvit |
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- pytorch |
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datasets: |
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- chinese-text-detection |
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pipeline_tag: image-segmentation |
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--- |
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# DBNet++ RepViT (Chinese) |
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Lightweight text detection model combining DBNet++ with RepViT backbone, optimized for efficient inference. Pretrained on **Chinese text detection datasets**. |
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## Model Details |
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| Component | Configuration | |
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|-----------|--------------| |
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| Architecture | DBNet++ (Differentiable Binarization) | |
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| Backbone | RepViT (lightweight ViT-inspired CNN) | |
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| Neck | RSEFPN (in: [48, 96, 192, 384], out: 96) | |
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| Head | DBNetPPHead (inner: 24, k: 50) | |
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| Parameters | ~3M | |
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| Input Size | 640x640 (flexible) | |
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## Training Data |
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This model was converted from [OpenOCR](https://github.com/Topdu/OpenOCR) pretrained weights, trained on **Chinese text detection datasets**. |
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**Recommended datasets for fine-tuning:** |
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- MSRA-TD500 (Chinese + English) |
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- ICDAR2017 RCTW (Chinese) |
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- CTW1500 |
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**Note:** For English-only text detection, fine-tuning on English datasets (ICDAR2015, Total-Text) is recommended. |
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## Usage |
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### With Hugging Face |
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```python |
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from huggingface_hub import hf_hub_download |
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import torch |
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# Download model |
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model_path = hf_hub_download( |
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repo_id="thisisiron/dbnetpp_repvit_ch", |
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filename="dbnetpp_repvit_ch.pth" |
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) |
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# Load weights |
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state_dict = torch.load(model_path, map_location="cpu") |
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``` |
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### With OCR-Factory |
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```python |
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import torch |
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from ocrfactory.models.detect import DBNetPP |
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# Build model |
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model = DBNetPP( |
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backbone={"name": "RepViT"}, |
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neck={ |
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"name": "RSEFPN", |
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"in_channels": [48, 96, 192, 384], |
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"out_channels": 96, |
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"shortcut": True |
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}, |
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head={ |
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"name": "DBNetPPHead", |
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"in_channels": 96, |
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"inner_channels": 24, |
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"k": 50, |
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"use_asf": False |
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} |
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) |
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# Load weights |
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state_dict = torch.load("dbnetpp_repvit_ch.pth", map_location="cpu") |
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model.load_state_dict(state_dict, strict=True) |
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model.eval() |
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# Inference |
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x = torch.randn(1, 3, 640, 640) |
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with torch.no_grad(): |
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output = model(x) |
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shrink_map = output["shrink_map"] # (1, 1, 640, 640) |
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``` |
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### Training Config (YAML) |
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```yaml |
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architecture: |
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backbone: |
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name: RepViT |
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neck: |
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name: RSEFPN |
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in_channels: [48, 96, 192, 384] |
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out_channels: 96 |
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shortcut: true |
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head: |
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name: DBNetPPHead |
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in_channels: 96 |
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inner_channels: 24 |
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k: 50 |
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use_asf: false |
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``` |
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## Performance |
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| Dataset | Precision | Recall | H-mean | |
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|---------|-----------|--------|--------| |
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| MSRA-TD500 | - | - | - | |
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*Performance metrics will be updated after benchmarking.* |
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## References |
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- **OpenOCR**: https://github.com/Topdu/OpenOCR |
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- **RepViT**: https://github.com/THU-MIG/RepViT |
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- **DBNet++**: [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304) |
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## License |
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Apache 2.0 |
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