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dbnetpp_repvit_ch

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dbnetpp_repvit_ch
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---
license: apache-2.0
language:
- zh
- en
tags:
- text-detection
- ocr
- dbnet
- repvit
- pytorch
datasets:
- chinese-text-detection
pipeline_tag: image-segmentation
---

# DBNet++ RepViT (Chinese)

Lightweight text detection model combining DBNet++ with RepViT backbone, optimized for efficient inference. Pretrained on **Chinese text detection datasets**.

## Model Details

| Component | Configuration |
|-----------|--------------|
| Architecture | DBNet++ (Differentiable Binarization) |
| Backbone | RepViT (lightweight ViT-inspired CNN) |
| Neck | RSEFPN (in: [48, 96, 192, 384], out: 96) |
| Head | DBNetPPHead (inner: 24, k: 50) |
| Parameters | ~3M |
| Input Size | 640x640 (flexible) |

## Training Data

This model was converted from [OpenOCR](https://github.com/Topdu/OpenOCR) pretrained weights, trained on **Chinese text detection datasets**.

**Recommended datasets for fine-tuning:**
- MSRA-TD500 (Chinese + English)
- ICDAR2017 RCTW (Chinese)
- CTW1500

**Note:** For English-only text detection, fine-tuning on English datasets (ICDAR2015, Total-Text) is recommended.

## Usage

### With Hugging Face

```python
from huggingface_hub import hf_hub_download
import torch

# Download model
model_path = hf_hub_download(
    repo_id="thisisiron/dbnetpp_repvit_ch",
    filename="dbnetpp_repvit_ch.pth"
)

# Load weights
state_dict = torch.load(model_path, map_location="cpu")
```

### With OCR-Factory

```python
import torch
from ocrfactory.models.detect import DBNetPP

# Build model
model = DBNetPP(
    backbone={"name": "RepViT"},
    neck={
        "name": "RSEFPN",
        "in_channels": [48, 96, 192, 384],
        "out_channels": 96,
        "shortcut": True
    },
    head={
        "name": "DBNetPPHead",
        "in_channels": 96,
        "inner_channels": 24,
        "k": 50,
        "use_asf": False
    }
)

# Load weights
state_dict = torch.load("dbnetpp_repvit_ch.pth", map_location="cpu")
model.load_state_dict(state_dict, strict=True)
model.eval()

# Inference
x = torch.randn(1, 3, 640, 640)
with torch.no_grad():
    output = model(x)
    shrink_map = output["shrink_map"]  # (1, 1, 640, 640)
```

### Training Config (YAML)

```yaml
architecture:
  backbone:
    name: RepViT
  neck:
    name: RSEFPN
    in_channels: [48, 96, 192, 384]
    out_channels: 96
    shortcut: true
  head:
    name: DBNetPPHead
    in_channels: 96
    inner_channels: 24
    k: 50
    use_asf: false
```

## Performance

| Dataset | Precision | Recall | H-mean |
|---------|-----------|--------|--------|
| MSRA-TD500 | - | - | - |

*Performance metrics will be updated after benchmarking.*

## References

- **OpenOCR**: https://github.com/Topdu/OpenOCR
- **RepViT**: https://github.com/THU-MIG/RepViT
- **DBNet++**: [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)

## License

Apache 2.0