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Args:
model: PyTorch model to be saved
save_dir: directory to save model and config
arch: architecture name
task: task name
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>>> from doctr.models import login_to_hub, push_to_hf_hub
>>> from doctr.models.recognition import crnn_mobilenet_v3_small
>>> login_to_hub()
>>> model = crnn_mobilenet_v3_small(pretrained=True)
>>> push_to_hf_hub(model, 'my-model', 'recognition', arch='crnn_mobilenet_v3_small')
Args:
model: PyTorch model to be saved
model_name: name of the model which is also the repository name
task: task name
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language: en
**Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch**
## Task: a_
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([''])
>>> # 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)
```
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t | fddi|}| | |S )aX Instantiate & load a pretrained model from HF hub.
>>> from doctr.models import from_hub
>>> model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn")
Args:
repo_id: HuggingFace model hub repo
kwargs: kwargs of `hf_hub_download` or `snapshot_download`
Returns:
Model loaded with the checkpoint
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