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‎.gitignore‎

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*.pyc
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*.ipynb_checkpoints
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__pycache__
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.vscode/

‎COCO_Image_Viewer.ipynb‎

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‎LICENSE‎

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COPYRIGHT
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All contributions by François Chollet:
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Copyright (c) 2015 - 2018, François Chollet.
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All rights reserved.
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All contributions by Google:
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Copyright (c) 2015 - 2018, Google, Inc.
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All rights reserved.
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All contributions by Microsoft:
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Copyright (c) 2017 - 2018, Microsoft, Inc.
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All rights reserved.
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All other contributions:
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Copyright (c) 2015 - 2018, the respective contributors.
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All rights reserved.
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Each contributor holds copyright over their respective contributions.
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The project versioning (Git) records all such contribution source information.
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LICENSE
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The MIT License (MIT)
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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‎README.md‎

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# [How to create custom COCO data set for object detection](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-object-detection/) | DLology blog
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## Quick start
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Then you can run the `voc2coco.py` script to generate a COCO data formatted JSON file for you.
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```
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python voc2coco.py ./data/VOC/Annotations ./data/coco/output.json
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```
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Then you can run the following Jupyter notebook to visualize the coco annotations. `COCO_Image_Viewer.ipynb`
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Further instruction on how to create your own datasets, read the [tutorial](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-object-detection/).

‎data/VOC/Annotations/0.xml‎

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<annotation>
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<folder>images</folder>
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<filename>0.jpg</filename>
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<path>E:\Learning_Resources\Deep_learning\my_data_sets\DATASETS_IMAGE\object_detection_demo\images\0.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>date</name>
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</bndbox>
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</object>
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<object>
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<name>fig</name>
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<xmin>368</xmin>
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<xmin>563</xmin>
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<ymax>250</ymax>
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</bndbox>
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</object>
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<object>
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<name>fig</name>
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<object>
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</annotation>

‎data/VOC/Annotations/1.xml‎

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<annotation>
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<folder>images</folder>
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<filename>1.jpg</filename>
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<path>E:\Learning_Resources\Deep_learning\my_data_sets\DATASETS_IMAGE\object_detection_demo\images\1.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>800</width>
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<height>600</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>fig</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<bndbox>
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<xmin>390</xmin>
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<ymin>61</ymin>
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</bndbox>
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</annotation>

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