Add doctr-dummy-tf-linknet-resnet34 model

.gitattributes

@@ -26,3 +26,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tf_model/weights.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,39 @@
+
+---
+language: en
+---
+
+<p align="center">
+<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
+</p>
+
+**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
+
+## Task: detection
+
+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": [
+    0.798,
+    0.785,
+    0.772
+  ],
+  "std": [
+    0.264,
+    0.2749,
+    0.287
+  ],
+  "input_shape": [
+    1024,
+    1024,
+    3
+  ],
+  "url": null,
+  "arch": "linknet_resnet34",
+  "task": "detection"
+}

tf_model/checkpoint

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+model_checkpoint_path: "weights"
+all_model_checkpoint_paths: "weights"

tf_model/weights.data-00000-of-00001

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+size 86690418