Add doctr-dummy-tf-mobilenet-v3-small-crop-orientation-v2 model

README.md

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+
+---
+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: classification
+
+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)
+```

config.json

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+{
+  "mean": [
+    0.694,
+    0.695,
+    0.693
+  ],
+  "std": [
+    0.299,
+    0.296,
+    0.301
+  ],
+  "input_shape": [
+    32,
+    32,
+    3
+  ],
+  "classes": [
+    0,
+    -90,
+    180,
+    90
+  ],
+  "url": "https://github.com/mindee/doctr/releases/download/v0.9.0/mobilenet_v3_small_crop_orientation-ef019b6b.weights.h5",
+  "num_classes": 4,
+  "arch": "mobilenet_v3_small_crop_orientation",
+  "task": "classification"
+}

tf_model.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef019b6b43f1b459aa97239ecb57c33339971d659735b5287eb3c35d937e8262
+size 6519936