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    "info": {
        "author": "Cl\u00e9ment Lafont",
        "author_email": "lafont.clem@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "# Dataset Downloader\n\n## Preview\n\nDataset_downloader allow you to download large dataset from multiple list of url, from [image-net](http://image-net.org) for example.\nYou can split the download into 2 folders, one for the training and one for the testing.\nFile are save into their class name, perfect for model training. It looks something like that:\n\n```\nroot:.\n|\n\u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20actest\n\u00e2\u201d\u201a   \u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20acaccerola\n\u00e2\u201d\u201a   \u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20acapple\n\u00e2\u201d\u201a   \u00e2\u201d\u201d\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20aclemon\n\u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20actrain\n\u00e2\u201d\u201a   \u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20acaccerola\n\u00e2\u201d\u201a   \u00e2\u201d\u0153\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20acapple\n\u00e2\u201d\u201a   \u00e2\u201d\u201d\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20aclemon\n```\n\n## Installation\n\nSimply install from pip:\n```\npip install dataset_downloader\n```\n\n## Config\n\nCreate a `dataset.json` file with the following content:\n\n```json\n{\n  \"outputTrain\": \"...\",\n  \"outputTest\": \"...\",\n  \"ratio\": ...,\n  \"classes\": {\n    \"class1\": [\n      \"http://url1\",\n      \"http://url2\"\n    ],\n    \"class2\": [\n      \"http://url1\",\n      \"http://url2\"\n    ],\n    \"class3\": \"list_images.txt\"\n  }\n}\n```\n\n* `outputTrain`: Output folder of the training images\n* `outputTest`: Output folder of the testing images\n* `ratio`: The ratio of training/testing images. 0.8 correspond of 80% of training images.\n* `classes`: List of classes with their urls. Urls can be a list of url, a file containing a list of urls or an url containing a list of urls\n\nAn exemple of file on a windows computer:\n\n```json\n  \"outputTrain\": \"D:/dataset/train\",\n  \"outputTest\": \"D:/dataset/test\",\n  \"ratio\": 0.8,\n  \"classes\": {\n    \"accerola\": [\n      \"http://tiachea.files.wordpress.com/2008/10/acerolas.jpg\",\n      \"http://www.jardimdeflores.com.br/floresefolhas/JPEGS/A56acerola5.JPG\",\n      \"http://farm2.staticflickr.com/1353/4602150961_177e096984_z.jpg\",\n    ],\n    \"apple\": [\n      \"http://www.naturalhealth365.com/images/apple.jpg\",\n      \"http://urbanext.illinois.edu/fruit/images/apple1.jpg\",\n      \"https://www.aroma-zone.com/cms//sites/default/files/plante-acerola.jpg\"\n    ],\n    \"lemon\": \"list_images.txt\",\n    \"watermelon\": \"https://gist.githubusercontent.com/johnrazeur/645787bc08a5aedd82da9573fbfa169a/raw/49cea1ee1438cecef8ac213b20f24e5ae02d4d78/watermelon.txt\"\n  }\n```\n\n## Run\n\nSimple call the dataset_downloader command:\n\n```bash\ncd yourdirectory\n# You must create the dataset.json file before\ndataset_downloader\n```\n\n",
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        "name": "dataset-downloader",
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