README.md

---
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": "detection_test",
  "epochs": 15,
  "batch_size": 2,
  "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": true,
  "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
}