{
  "id": 166845,
  "title": "Filenames like 4.22E+49 , 4.87E+13 , 30314634 etc. present in csv",
  "url": "/competitions/global-wheat-detection/discussion/166845",
  "author_name": "",
  "post_date": "2020-07-14T08:45:57.356007200Z",
  "votes": null,
  "comment_count": 4,
  "views": 0,
  "content": "<p>When I iterated over the train.csv file I found these image_id names\n7.23E+14\n4.97E+15\n4.87E+13\n4.41E+74\n4.22E+49\n6.07E+29\n6.30E+12\n7.31E+36\n3.70E+17\n3.84E+07\n34090252\n5.96E+60\n2.28E+58\n3.54E+08\n9.54E+135\n15939012\n3.84E+94\n3.96E+199\n13669953\n3.66E+64\n59125072\n8.74E+11\n2.44E+12\n30314634\n27086635</p>\n\n<p>these image_ids are not linked with any of the image files present in the train folder.\nCan anyone lease help me know what these files are related to?  </p>",
  "messages": [
    {
      "id": "928826",
      "postDate": "07/14/2020 08:45:57",
      "content": "<p>When I iterated over the train.csv file I found these image_id names\n7.23E+14\n4.97E+15\n4.87E+13\n4.41E+74\n4.22E+49\n6.07E+29\n6.30E+12\n7.31E+36\n3.70E+17\n3.84E+07\n34090252\n5.96E+60\n2.28E+58\n3.54E+08\n9.54E+135\n15939012\n3.84E+94\n3.96E+199\n13669953\n3.66E+64\n59125072\n8.74E+11\n2.44E+12\n30314634\n27086635</p>\n\n<p>these image_ids are not linked with any of the image files present in the train folder.\nCan anyone lease help me know what these files are related to?  </p>",
      "rawMarkdown": "When I iterated over the train.csv file I found these image_id names\n7.23E+14\n4.97E+15\n4.87E+13\n4.41E+74\n4.22E+49\n6.07E+29\n6.30E+12\n7.31E+36\n3.70E+17\n3.84E+07\n34090252\n5.96E+60\n2.28E+58\n3.54E+08\n9.54E+135\n15939012\n3.84E+94\n3.96E+199\n13669953\n3.66E+64\n59125072\n8.74E+11\n2.44E+12\n30314634\n27086635\n\nthese image_ids are not linked with any of the image files present in the train folder.\nCan anyone lease help me know what these files are related to?",
      "votes": null
    },
    {
      "id": "928831",
      "postDate": "07/14/2020 08:49:00",
      "content": "<p>Perhaps you've opened the CSV in Excel or LibreOffice which converts strings into numbers and inadvertently saved over the original train.csv. The original file does not contain any image_ids in this form</p>",
      "rawMarkdown": "Perhaps you've opened the CSV in Excel or LibreOffice which converts strings into numbers and inadvertently saved over the original train.csv. The original file does not contain any image_ids in this form",
      "votes": null
    },
    {
      "id": "928879",
      "postDate": "07/14/2020 09:48:46",
      "content": "<p>There are around 3373 images id present in csv files and around 3422 images in train folder.Image which are not present in csv file is images with no wheat heads. </p>",
      "rawMarkdown": "There are around 3373 images id present in csv files and around 3422 images in train folder.Image which are not present in csv file is images with no wheat heads.",
      "votes": null
    },
    {
      "id": "928924",
      "postDate": "07/14/2020 10:27:23",
      "content": "<p>Does all the image_ids present in train.csv file are also present as images in train folder? I know the fact that some images in the training folder are not having any bounding boxes annotations.</p>",
      "rawMarkdown": "Does all the image_ids present in train.csv file are also present as images in train folder? I know the fact that some images in the training folder are not having any bounding boxes annotations.",
      "votes": null
    },
    {
      "id": "929631",
      "postDate": "07/14/2020 20:03:15",
      "content": "<p>Yes, all image_ids in the train.csv are present in the train folder. However, not all images in the train folder are in the csv as 49 images are negative (contain no wheat heads). You can leave these out or find a way to pass them to your network like I show <a href=\"https://www.kaggle.com/c/global-wheat-detection/discussion/152260#929629\">here</a>.</p>",
      "rawMarkdown": "Yes, all image_ids in the train.csv are present in the train folder. However, not all images in the train folder are in the csv as 49 images are negative (contain no wheat heads). You can leave these out or find a way to pass them to your network like I show [here](https://www.kaggle.com/c/global-wheat-detection/discussion/152260#929629).",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 928831,
      "author_name": "matthewmasters",
      "author_url": "",
      "post_date": "07/14/2020 08:49:00",
      "content": "<p>Perhaps you've opened the CSV in Excel or LibreOffice which converts strings into numbers and inadvertently saved over the original train.csv. The original file does not contain any image_ids in this form</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 928879,
      "author_name": "ravi02516",
      "author_url": "",
      "post_date": "07/14/2020 09:48:46",
      "content": "<p>There are around 3373 images id present in csv files and around 3422 images in train folder.Image which are not present in csv file is images with no wheat heads. </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 928924,
      "author_name": "soumochatterjee",
      "author_url": "",
      "post_date": "07/14/2020 10:27:23",
      "content": "<p>Does all the image_ids present in train.csv file are also present as images in train folder? I know the fact that some images in the training folder are not having any bounding boxes annotations.</p>",
      "votes": null,
      "replies": [
        {
          "id": 929631,
          "author_name": "matthewmasters",
          "author_url": "",
          "post_date": "07/14/2020 20:03:15",
          "content": "<p>Yes, all image_ids in the train.csv are present in the train folder. However, not all images in the train folder are in the csv as 49 images are negative (contain no wheat heads). You can leave these out or find a way to pass them to your network like I show <a href=\"https://www.kaggle.com/c/global-wheat-detection/discussion/152260#929629\">here</a>.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "928826": "When I iterated over the train.csv file I found these image_id names\n7.23E+14\n4.97E+15\n4.87E+13\n4.41E+74\n4.22E+49\n6.07E+29\n6.30E+12\n7.31E+36\n3.70E+17\n3.84E+07\n34090252\n5.96E+60\n2.28E+58\n3.54E+08\n9.54E+135\n15939012\n3.84E+94\n3.96E+199\n13669953\n3.66E+64\n59125072\n8.74E+11\n2.44E+12\n30314634\n27086635\n\nthese image_ids are not linked with any of the image files present in the train folder.\nCan anyone lease help me know what these files are related to?",
    "928831": "Perhaps you've opened the CSV in Excel or LibreOffice which converts strings into numbers and inadvertently saved over the original train.csv. The original file does not contain any image_ids in this form",
    "928879": "There are around 3373 images id present in csv files and around 3422 images in train folder.Image which are not present in csv file is images with no wheat heads.",
    "928924": "Does all the image_ids present in train.csv file are also present as images in train folder? I know the fact that some images in the training folder are not having any bounding boxes annotations.",
    "929631": "Yes, all image_ids in the train.csv are present in the train folder. However, not all images in the train folder are in the csv as 49 images are negative (contain no wheat heads). You can leave these out or find a way to pass them to your network like I show [here](https://www.kaggle.com/c/global-wheat-detection/discussion/152260#929629)."
  },
  "source": "meta"
}