{
  "id": 94660,
  "title": "[0.15-1.5GB] Re-sized and compressed validation data-set (512x, 256x, 128x)",
  "url": "/competitions/open-images-2019-object-detection/discussion/94660",
  "author_name": "",
  "post_date": "2019-06-06T02:39:47.916023Z",
  "votes": 29,
  "comment_count": 5,
  "views": 0,
  "content": "<p><strong>Why download 12GB when you can download ~200MB?</strong> <em>Hooray</em></p>\n\n<p>Hey everyone,</p>\n\n<p>Please find below the re-sized and compressed <code>validation</code> data-set, with the <code>train</code> data-set coming soon. As always, do let me know if you need anything specific, or if there are mistakes.</p>\n\n<p><strong>Note:</strong> These dimensions are not proportional, since the images are not exact squares. If there is demand for a forced-proportional data-set, I'd be happy to share that as well.</p>\n\n<h1>Downloads</h1>\n\n<p><strong>Original data-set</strong>\nUncompressed size: <code>~20GB</code></p>\n\n<p><strong><code>512x</code> data-set</strong>\nUncompressed size: <code>1.5GB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/15FOZRVDEu8VZsIJ5nY5Y63A27kKtXoBu/view?usp=sharing\">Drive</a></p>\n\n<p><strong><code>256x</code> data-set</strong>\nUncompressed size: <code>500MB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/1YMRFqh1hIYFEhNLJP8oKB-9vrmAjTloj/view?usp=sharing\">Drive</a></p>\n\n<p><strong><code>128x</code> data-set</strong>\nUncompressed size: <code>~200MB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/1uYY3hZFRZAhek2cKL2IkmzXkSjc7OIxY/view?usp=sharing\">Drive</a></p>\n\n<h1>Additional information</h1>\n\n<p>For <a href=\"https://www.kaggle.com/c/landmark-recognition-2019/discussion/91770\">process, limitations, and specific download instructions</a> (if you're using a CLI, these might be easier), please refer to the post I made for the last competition of this sort. The instructions are the same.</p>\n\n<p>Cheers!</p>\n\n<p>Anish</p>",
  "messages": [
    {
      "id": "545895",
      "postDate": "06/06/2019 02:39:47",
      "content": "<p><strong>Why download 12GB when you can download ~200MB?</strong> <em>Hooray</em></p>\n\n<p>Hey everyone,</p>\n\n<p>Please find below the re-sized and compressed <code>validation</code> data-set, with the <code>train</code> data-set coming soon. As always, do let me know if you need anything specific, or if there are mistakes.</p>\n\n<p><strong>Note:</strong> These dimensions are not proportional, since the images are not exact squares. If there is demand for a forced-proportional data-set, I'd be happy to share that as well.</p>\n\n<h1>Downloads</h1>\n\n<p><strong>Original data-set</strong>\nUncompressed size: <code>~20GB</code></p>\n\n<p><strong><code>512x</code> data-set</strong>\nUncompressed size: <code>1.5GB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/15FOZRVDEu8VZsIJ5nY5Y63A27kKtXoBu/view?usp=sharing\">Drive</a></p>\n\n<p><strong><code>256x</code> data-set</strong>\nUncompressed size: <code>500MB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/1YMRFqh1hIYFEhNLJP8oKB-9vrmAjTloj/view?usp=sharing\">Drive</a></p>\n\n<p><strong><code>128x</code> data-set</strong>\nUncompressed size: <code>~200MB</code>\nDownload link: <a href=\"https://drive.google.com/file/d/1uYY3hZFRZAhek2cKL2IkmzXkSjc7OIxY/view?usp=sharing\">Drive</a></p>\n\n<h1>Additional information</h1>\n\n<p>For <a href=\"https://www.kaggle.com/c/landmark-recognition-2019/discussion/91770\">process, limitations, and specific download instructions</a> (if you're using a CLI, these might be easier), please refer to the post I made for the last competition of this sort. The instructions are the same.</p>\n\n<p>Cheers!</p>\n\n<p>Anish</p>",
      "rawMarkdown": "**Why download 12GB when you can download ~200MB?** *Hooray*\n\nHey everyone,\n\nPlease find below the re-sized and compressed `validation` data-set, with the `train` data-set coming soon. As always, do let me know if you need anything specific, or if there are mistakes.\n\n**Note:** These dimensions are not proportional, since the images are not exact squares. If there is demand for a forced-proportional data-set, I'd be happy to share that as well.\n\n# Downloads\n\n**Original data-set**\nUncompressed size: `~20GB`\n\n**`512x` data-set**\nUncompressed size: `1.5GB`\nDownload link: [Drive](https://drive.google.com/file/d/15FOZRVDEu8VZsIJ5nY5Y63A27kKtXoBu/view?usp=sharing)\n\n**`256x` data-set**\nUncompressed size: `500MB`\nDownload link: [Drive](https://drive.google.com/file/d/1YMRFqh1hIYFEhNLJP8oKB-9vrmAjTloj/view?usp=sharing)\n\n**`128x` data-set**\nUncompressed size: `~200MB`\nDownload link: [Drive](https://drive.google.com/file/d/1uYY3hZFRZAhek2cKL2IkmzXkSjc7OIxY/view?usp=sharing)\n\n# Additional information\nFor [process, limitations, and specific download instructions](https://www.kaggle.com/c/landmark-recognition-2019/discussion/91770) (if you're using a CLI, these might be easier), please refer to the post I made for the last competition of this sort. The instructions are the same.\n\nCheers!\n\nAnish",
      "votes": null
    },
    {
      "id": "548533",
      "postDate": "06/09/2019 14:34:22",
      "content": "<p>Hi Anish, please share forced proportional datasets. It will help to overcome train burden </p>",
      "rawMarkdown": "Hi Anish, please share forced proportional datasets. It will help to overcome train burden",
      "votes": null
    },
    {
      "id": "548537",
      "postDate": "06/09/2019 14:45:01",
      "content": "<p>Hey! Sure, I am currently working on downloading and re-sizing the train data-set, but I will start to re-size to forced proportional aspect ratios as well.</p>",
      "rawMarkdown": "Hey! Sure, I am currently working on downloading and re-sizing the train data-set, but I will start to re-size to forced proportional aspect ratios as well.",
      "votes": null
    },
    {
      "id": "558878",
      "postDate": "06/23/2019 05:13:06",
      "content": "<p>Hi Anish,please share the train dataset as well.</p>",
      "rawMarkdown": "Hi Anish,please share the train dataset as well.",
      "votes": null
    },
    {
      "id": "560082",
      "postDate": "06/25/2019 02:55:23",
      "content": "<p>Hi,Anish,I found there only has 41620 images in the 512x,not the original image 99999!</p>",
      "rawMarkdown": "Hi,Anish,I found there only has 41620 images in the 512x,not the original image 99999!",
      "votes": null
    },
    {
      "id": "573491",
      "postDate": "07/12/2019 10:41:34",
      "content": "<p>Hi Anish, did you manage to resize also the train dataset ?  thanks !</p>",
      "rawMarkdown": "Hi Anish, did you manage to resize also the train dataset ?  thanks !",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 548533,
      "author_name": "validmodel",
      "author_url": "",
      "post_date": "06/09/2019 14:34:22",
      "content": "<p>Hi Anish, please share forced proportional datasets. It will help to overcome train burden </p>",
      "votes": null,
      "replies": [
        {
          "id": 548537,
          "author_name": "anishagnihotri",
          "author_url": "",
          "post_date": "06/09/2019 14:45:01",
          "content": "<p>Hey! Sure, I am currently working on downloading and re-sizing the train data-set, but I will start to re-size to forced proportional aspect ratios as well.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 558878,
      "author_name": "vikashpathak",
      "author_url": "",
      "post_date": "06/23/2019 05:13:06",
      "content": "<p>Hi Anish,please share the train dataset as well.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 560082,
      "author_name": "bcwang",
      "author_url": "",
      "post_date": "06/25/2019 02:55:23",
      "content": "<p>Hi,Anish,I found there only has 41620 images in the 512x,not the original image 99999!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 573491,
      "author_name": "nicupetridean",
      "author_url": "",
      "post_date": "07/12/2019 10:41:34",
      "content": "<p>Hi Anish, did you manage to resize also the train dataset ?  thanks !</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "545895": "**Why download 12GB when you can download ~200MB?** *Hooray*\n\nHey everyone,\n\nPlease find below the re-sized and compressed `validation` data-set, with the `train` data-set coming soon. As always, do let me know if you need anything specific, or if there are mistakes.\n\n**Note:** These dimensions are not proportional, since the images are not exact squares. If there is demand for a forced-proportional data-set, I'd be happy to share that as well.\n\n# Downloads\n\n**Original data-set**\nUncompressed size: `~20GB`\n\n**`512x` data-set**\nUncompressed size: `1.5GB`\nDownload link: [Drive](https://drive.google.com/file/d/15FOZRVDEu8VZsIJ5nY5Y63A27kKtXoBu/view?usp=sharing)\n\n**`256x` data-set**\nUncompressed size: `500MB`\nDownload link: [Drive](https://drive.google.com/file/d/1YMRFqh1hIYFEhNLJP8oKB-9vrmAjTloj/view?usp=sharing)\n\n**`128x` data-set**\nUncompressed size: `~200MB`\nDownload link: [Drive](https://drive.google.com/file/d/1uYY3hZFRZAhek2cKL2IkmzXkSjc7OIxY/view?usp=sharing)\n\n# Additional information\nFor [process, limitations, and specific download instructions](https://www.kaggle.com/c/landmark-recognition-2019/discussion/91770) (if you're using a CLI, these might be easier), please refer to the post I made for the last competition of this sort. The instructions are the same.\n\nCheers!\n\nAnish",
    "548533": "Hi Anish, please share forced proportional datasets. It will help to overcome train burden",
    "548537": "Hey! Sure, I am currently working on downloading and re-sizing the train data-set, but I will start to re-size to forced proportional aspect ratios as well.",
    "558878": "Hi Anish,please share the train dataset as well.",
    "560082": "Hi,Anish,I found there only has 41620 images in the 512x,not the original image 99999!",
    "573491": "Hi Anish, did you manage to resize also the train dataset ?  thanks !"
  },
  "source": "meta"
}