{
  "id": 222105,
  "title": "What is Image Augmentation and why is it useful?",
  "url": "/competitions/cassava-leaf-disease-classification/discussion/222105",
  "author_name": "",
  "post_date": "2021-02-25T09:55:36.243536Z",
  "votes": -13,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Image augmentation is a technique that is used to artificially expand the data-set. This is helpful when we are given a data-set with very few data samples. In case of Deep Learning, this situation is bad as the model tends to over-fit when we train it on limited number of data samples.</p>\n<p>Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.</p>\n<p>The benefits of data augmentation are two :-</p>\n<ol>\n<li>The first is the ability to generate ‘more data’ from limited data.</li>\n<li>If our model is overfitting, it will not know how to generalize and, therefore, will be less efficient.</li>\n</ol>",
  "messages": [
    {
      "id": "1217764",
      "postDate": "02/25/2021 09:55:36",
      "content": "<p>Image augmentation is a technique that is used to artificially expand the data-set. This is helpful when we are given a data-set with very few data samples. In case of Deep Learning, this situation is bad as the model tends to over-fit when we train it on limited number of data samples.</p>\n<p>Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.</p>\n<p>The benefits of data augmentation are two :-</p>\n<ol>\n<li>The first is the ability to generate ‘more data’ from limited data.</li>\n<li>If our model is overfitting, it will not know how to generalize and, therefore, will be less efficient.</li>\n</ol>",
      "rawMarkdown": "Image augmentation is a technique that is used to artificially expand the data-set. This is helpful when we are given a data-set with very few data samples. In case of Deep Learning, this situation is bad as the model tends to over-fit when we train it on limited number of data samples.\n\nImage data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.\n\nThe benefits of data augmentation are two :-\n1. The first is the ability to generate ‘more data’ from limited data.\n2. If our model is overfitting, it will not know how to generalize and, therefore, will be less efficient.",
      "votes": null
    },
    {
      "id": "1217981",
      "postDate": "02/25/2021 13:13:10",
      "content": "<p>Can you please stop spamming on Kaggle discussion </p>",
      "rawMarkdown": "Can you please stop spamming on Kaggle discussion",
      "votes": null
    },
    {
      "id": "1218026",
      "postDate": "02/25/2021 13:52:26",
      "content": "<p>What  are you doing? man  . Are  you  mad ?</p>",
      "rawMarkdown": "What  are you doing? man  . Are  you  mad ?",
      "votes": null
    },
    {
      "id": "1484865",
      "postDate": "08/21/2021 16:13:46",
      "content": "<p>We can google it, thanks. Stop spamming</p>",
      "rawMarkdown": "We can google it, thanks. Stop spamming",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1217981,
      "author_name": "mithilsalunkhe",
      "author_url": "",
      "post_date": "02/25/2021 13:13:10",
      "content": "<p>Can you please stop spamming on Kaggle discussion </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1218026,
      "author_name": "xujingzhao",
      "author_url": "",
      "post_date": "02/25/2021 13:52:26",
      "content": "<p>What  are you doing? man  . Are  you  mad ?</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1484865,
      "author_name": "roboserg",
      "author_url": "",
      "post_date": "08/21/2021 16:13:46",
      "content": "<p>We can google it, thanks. Stop spamming</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1217764": "Image augmentation is a technique that is used to artificially expand the data-set. This is helpful when we are given a data-set with very few data samples. In case of Deep Learning, this situation is bad as the model tends to over-fit when we train it on limited number of data samples.\n\nImage data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.\n\nThe benefits of data augmentation are two :-\n1. The first is the ability to generate ‘more data’ from limited data.\n2. If our model is overfitting, it will not know how to generalize and, therefore, will be less efficient.",
    "1217981": "Can you please stop spamming on Kaggle discussion",
    "1218026": "What  are you doing? man  . Are  you  mad ?",
    "1484865": "We can google it, thanks. Stop spamming"
  },
  "source": "meta"
}