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Competition/ObjectDetectionRecognition/README.md

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@@ -30,6 +30,10 @@ The goal is to improve OpenStreetMap by adding high quality baseball, soccer, te
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* [demo.ipynb](/demo.ipynb) Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images.
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It includes code to run object detection and instance segmentation on arbitrary images.
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* [Video-Demo-Mask_RCNN.ipynb](/Video-Demo-Mask_RCNN.ipynb.ipynb) Is the another easiest way to start the stream data. It shows an example of using a model pre-trained on MS COCO to segment objects from your own webcam.
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It includes code to run object detection and instance segmentation on arbitrary images.
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This is an additional example which does not include in original repository. It refers from some other resources into this Jupyter notebook.
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* [train_shapes.ipynb](train_shapes.ipynb) shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset.
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* ([model.py](model.py), [utils.py](utils.py), [config.py](config.py)): These files contain the main Mask RCNN implementation.

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