ayymen/crnn_mobilenet_v3_large_zgh

Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch

Task: recognition

https://github.com/mindee/doctr

Example usage:

>>> 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

{ "arch": "crnn_mobilenet_v3_large", "train_path": "train", "val_path": "val", "train_samples": 1000, "val_samples": 20, "font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf", "min_chars": 1, "max_chars": 12, "name": "crnn_mobilenet_v3_large_zgh", "epochs": 2, "batch_size": 64, "device": null, "input_size": 32, "lr": 0.001, "weight_decay": 0, "workers": 2, "resume": null, "vocab": "zgh", "test_only": false, "show_samples": false, "wb": true, "push_to_hub": true, "pretrained": true, "sched": "cosine", "amp": false, "find_lr": false }