Add doctr-dummy-tf-resnet50-v2 model
Browse files- README.md +39 -0
- config.json +149 -0
- tf_model.weights.h5 +3 -0
README.md
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---
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language: en
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---
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<p align="center">
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<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
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</p>
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**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
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## Task: classification
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https://github.com/mindee/doctr
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### Example usage:
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```python
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>>> from doctr.io import DocumentFile
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>>> from doctr.models import ocr_predictor, from_hub
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>>> img = DocumentFile.from_images(['<image_path>'])
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>>> # Load your model from the hub
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>>> model = from_hub('mindee/my-model')
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>>> # Pass it to the predictor
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>>> # If your model is a recognition model:
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>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
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>>> reco_arch=model,
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>>> pretrained=True)
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>>> # If your model is a detection model:
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>>> predictor = ocr_predictor(det_arch=model,
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>>> reco_arch='crnn_mobilenet_v3_small',
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>>> pretrained=True)
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>>> # Get your predictions
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>>> res = predictor(img)
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```
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config.json
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{
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"mean": [
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0.694,
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0.695,
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0.693
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],
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"std": [
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0.299,
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0.296,
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0.301
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],
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"input_shape": [
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32,
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32,
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3
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],
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"classes": [
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"0",
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"1",
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"2",
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"3",
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"4",
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"5",
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"6",
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"7",
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"8",
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"9",
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"a",
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"b",
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"c",
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"d",
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"e",
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"f",
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"g",
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"h",
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"i",
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"j",
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"k",
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"l",
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"m",
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"n",
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"o",
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"p",
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"q",
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"r",
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"s",
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"t",
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"u",
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"v",
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"w",
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"x",
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"y",
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"z",
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"A",
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"B",
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"C",
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"D",
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"E",
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"F",
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"G",
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"H",
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"I",
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"J",
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"K",
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"L",
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"M",
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"N",
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"O",
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"P",
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"Q",
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"R",
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"S",
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"T",
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"U",
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"V",
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"W",
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"X",
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"Y",
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"Z",
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"!",
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"\"",
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"#",
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"$",
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"%",
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"&",
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"'",
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"*",
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"+",
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",",
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"/",
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":",
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";",
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"<",
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">",
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"[",
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"£",
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"€",
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"¥",
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"¢",
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"฿",
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"à",
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"â",
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"é",
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"è",
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"ê",
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"ë",
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"î",
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"ï",
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"ô",
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"ù",
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"û",
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"ü",
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"ç",
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"À",
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"Â",
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"É",
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"È",
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"Ê",
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"Ë",
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"Î",
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"Ï",
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"Ô",
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"Ù",
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"Û",
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"Ü",
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"Ç"
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],
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"url": "https://github.com/mindee/doctr/releases/download/v0.9.0/resnet50-82358f34.weights.h5",
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"num_classes": 126,
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"arch": "resnet50",
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"task": "classification"
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}
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tf_model.weights.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:82358f3437c71ba6e5407419c9493ce0bf93ab8c645396e6293bda389ee8499d
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size 95836632
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