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---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"train_path": "/workspace/donut_train/doctr/train/",
"val_path": "/workspace/donut_train/doctr/val/",
"arch": "db_resnet50",
"name": null,
"epochs": 5,
"batch_size": 10,
"device": 0,
"save_interval_epoch": false,
"input_size": 1024,
"lr": 0.001,
"weight_decay": 0,
"workers": 16,
"resume": null,
"test_only": false,
"freeze_backbone": false,
"show_samples": false,
"wb": false,
"push_to_hub": true,
"pretrained": false,
"rotation": false,
"eval_straight": false,
"sched": "poly",
"amp": false,
"find_lr": false,
"early_stop": false,
"early_stop_epochs": 5,
"early_stop_delta": 0.01
} |