{
  "id": 100377,
  "title": "Help getting started",
  "url": "/competitions/open-images-2019-object-detection/discussion/100377",
  "author_name": "",
  "post_date": "2019-07-18T07:36:24.459421900Z",
  "votes": 4,
  "comment_count": 6,
  "views": 0,
  "content": "<p>Hey everyone! \nI'm relatively a beginner to Kaggle and the competetions here. I have around 6 months of experience with ML. The most complex project I have done is to use Facenet for facial recognition. I have also played around with Keras., although I'm still learning and figuring my way out.\nHow can I get started with this challenge? Can I use frameworks like YOLO to achieve the necessary results? (As in this case we need to come up with the bounding boxes)</p>",
  "messages": [
    {
      "id": "578838",
      "postDate": "07/18/2019 07:36:24",
      "content": "<p>Hey everyone! \nI'm relatively a beginner to Kaggle and the competetions here. I have around 6 months of experience with ML. The most complex project I have done is to use Facenet for facial recognition. I have also played around with Keras., although I'm still learning and figuring my way out.\nHow can I get started with this challenge? Can I use frameworks like YOLO to achieve the necessary results? (As in this case we need to come up with the bounding boxes)</p>",
      "rawMarkdown": "Hey everyone! \nI'm relatively a beginner to Kaggle and the competetions here. I have around 6 months of experience with ML. The most complex project I have done is to use Facenet for facial recognition. I have also played around with Keras., although I'm still learning and figuring my way out.\nHow can I get started with this challenge? Can I use frameworks like YOLO to achieve the necessary results? (As in this case we need to come up with the bounding boxes)",
      "votes": null
    },
    {
      "id": "579991",
      "postDate": "07/19/2019 14:11:43",
      "content": "<p>I would suggest you start with last year winning solutions of the same competition <a href=\"https://www.kaggle.com/c/open-images-2019-object-detection/discussion/94334\">here</a>.</p>",
      "rawMarkdown": "I would suggest you start with last year winning solutions of the same competition [here](https://www.kaggle.com/c/open-images-2019-object-detection/discussion/94334).",
      "votes": null
    },
    {
      "id": "580068",
      "postDate": "07/19/2019 16:23:19",
      "content": "<p>Thanks a lot for the suggestion! I will definitely check up on that!</p>",
      "rawMarkdown": "Thanks a lot for the suggestion! I will definitely check up on that!",
      "votes": null
    },
    {
      "id": "580082",
      "postDate": "07/19/2019 16:40:14",
      "content": "<p>You're welcome!</p>",
      "rawMarkdown": "You're welcome!",
      "votes": null
    },
    {
      "id": "585422",
      "postDate": "07/27/2019 12:28:37",
      "content": "<p>Thank you so much <a href=\"/tayorm\">@tayorm</a> </p>",
      "rawMarkdown": "Thank you so much @tayorm",
      "votes": null
    },
    {
      "id": "585469",
      "postDate": "07/27/2019 14:10:39",
      "content": "<p>You're right and then reverse-engineer the process! Fantastic. Thank you!</p>",
      "rawMarkdown": "You're right and then reverse-engineer the process! Fantastic. Thank you!",
      "votes": null
    },
    {
      "id": "593770",
      "postDate": "08/07/2019 05:01:57",
      "content": "<p>Thank you so much for the suggestion !!</p>",
      "rawMarkdown": "Thank you so much for the suggestion !!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 579991,
      "author_name": "tayorm",
      "author_url": "",
      "post_date": "07/19/2019 14:11:43",
      "content": "<p>I would suggest you start with last year winning solutions of the same competition <a href=\"https://www.kaggle.com/c/open-images-2019-object-detection/discussion/94334\">here</a>.</p>",
      "votes": null,
      "replies": [
        {
          "id": 580068,
          "author_name": "fillerink",
          "author_url": "",
          "post_date": "07/19/2019 16:23:19",
          "content": "<p>Thanks a lot for the suggestion! I will definitely check up on that!</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 580082,
          "author_name": "tayorm",
          "author_url": "",
          "post_date": "07/19/2019 16:40:14",
          "content": "<p>You're welcome!</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 585422,
          "author_name": "lati23",
          "author_url": "",
          "post_date": "07/27/2019 12:28:37",
          "content": "<p>Thank you so much <a href=\"/tayorm\">@tayorm</a> </p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 585469,
          "author_name": "johnnunez245",
          "author_url": "",
          "post_date": "07/27/2019 14:10:39",
          "content": "<p>You're right and then reverse-engineer the process! Fantastic. Thank you!</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 593770,
          "author_name": "nanditab35",
          "author_url": "",
          "post_date": "08/07/2019 05:01:57",
          "content": "<p>Thank you so much for the suggestion !!</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "578838": "Hey everyone! \nI'm relatively a beginner to Kaggle and the competetions here. I have around 6 months of experience with ML. The most complex project I have done is to use Facenet for facial recognition. I have also played around with Keras., although I'm still learning and figuring my way out.\nHow can I get started with this challenge? Can I use frameworks like YOLO to achieve the necessary results? (As in this case we need to come up with the bounding boxes)",
    "579991": "I would suggest you start with last year winning solutions of the same competition [here](https://www.kaggle.com/c/open-images-2019-object-detection/discussion/94334).",
    "580068": "Thanks a lot for the suggestion! I will definitely check up on that!",
    "580082": "You're welcome!",
    "585422": "Thank you so much @tayorm",
    "585469": "You're right and then reverse-engineer the process! Fantastic. Thank you!",
    "593770": "Thank you so much for the suggestion !!"
  },
  "source": "meta"
}