ayymen/crnn_mobilenet_v3_large_zgh

<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: 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": "crnnmobilenetv3large", "trainpath": "train", "valpath": "val", "trainsamples": 1000, "valsamples": 20, "font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf", "minchars": 1, "maxchars": 12, "name": "crnnmobilenetv3largezgh", "epochs": 2, "batchsize": 64, "device": null, "inputsize": 32, "lr": 0.001, "weightdecay": 0, "workers": 2, "resume": null, "vocab": "zgh", "testonly": false, "showsamples": false, "wb": true, "pushtohub": true, "pretrained": true, "sched": "cosine", "amp": false, "find_lr": false }