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    "info": {
        "author": "Michael Sugimura",
        "author_email": "deepak.kumar.iet@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "\n\ud83d\udc26 multi_class_pigeon - Quickly annotate data on Jupyter\n========================\n\nThis repo is a simple multiclass image extenstion of the pigeon repo by\n@agermanidis located [here](https://github.com/agermanidis/pigeon). Credit goes to them for building\nthe annotate function for images, text, and regression for multi-class type problems. All I am doing is extending it to multi-label and multi-task type problems.\n\n## Notes\n__________\nAs of now I just have this as a repo... so to use it you can clone it and either run notebooks from inside it and use the below format or modify the imports to point at the directory containing the annotation script.\n\n\nExamples\n-----\n```\nfrom pigeon import multi_label_annotate\nfrom IPython.display import display, Image\n\nannotations = multi_label_annotate(\n    ['assets/altera.jpg', 'assets/chibi_gil.jpg','assets/chibi_saber.jpg'],\n    options={'cute':['yes','no'], 'saber':['yes','no'],'colors':['blue','gold','white','red']},\n    display_fn=lambda filename: display(Image(filename))\n    )\n```\nPreview:\n\n![alt text](/assets/sample_usage3.gif)\n\n## Additional Notes\n____\nI added some additional buttons as well to the multi-label script. Since we want to be able to select more than one category I added a few extra buttons.\n\n- `done` button to press after all relevant fields have been clicked. \n\n- `back` button in case you want to go back to a previous image. WARNING this will erase that item from the dictionary so you will have to click through all the classes you want to mark for that previous image again. \n\n- `clear current` will delete the current image from the dictionary on the back end so you can re enter the classes in case you messed one up.\n\n\n",
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        "name": "multi-label-pigeon-jupyter",
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        "summary": "A way to label multi label image datasets in jupyter",
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