{
  "id": 576246,
  "title": "score concerns",
  "url": "/competitions/beyond-visible-spectrum-ai-for-agriculture-2025/discussion/576246",
  "author_name": "",
  "post_date": "2025-05-03T19:01:55.145761200Z",
  "votes": 5,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Hi! I’m really puzzled by the results. After a lot of experiments, it seems like the labels in the train file have no real significance-they feel almost random, and any attempt to actually model the data (even with domain-specific, agricultural approaches) just makes the score worse. In fact, just predicting the mean (around 49–50) for every patch seems to be the glass ceiling, and nothing I try can beat it.</p>",
  "messages": [
    {
      "id": "3193068",
      "postDate": "05/03/2025 19:01:55",
      "content": "<p>Hi! I’m really puzzled by the results. After a lot of experiments, it seems like the labels in the train file have no real significance-they feel almost random, and any attempt to actually model the data (even with domain-specific, agricultural approaches) just makes the score worse. In fact, just predicting the mean (around 49–50) for every patch seems to be the glass ceiling, and nothing I try can beat it.</p>",
      "rawMarkdown": "Hi! I’m really puzzled by the results. After a lot of experiments, it seems like the labels in the train file have no real significance-they feel almost random, and any attempt to actually model the data (even with domain-specific, agricultural approaches) just makes the score worse. In fact, just predicting the mean (around 49–50) for every patch seems to be the glass ceiling, and nothing I try can beat it.",
      "votes": null
    },
    {
      "id": "3193131",
      "postDate": "05/03/2025 22:57:18",
      "content": "<p>Yep… such a cool competition, but it seems there's a problem with how the data was extracted</p>",
      "rawMarkdown": "Yep... such a cool competition, but it seems there's a problem with how the data was extracted",
      "votes": null
    },
    {
      "id": "3193334",
      "postDate": "05/04/2025 08:22:54",
      "content": "<p>You're right — in this case, it would be ideal to leverage well-researched models like SSRN, SSATNet, and SugarViT, all of which have open-source implementations available on GitHub. In addition, we can enhance model performance through spectral augmentation techniques such as ChannelDropout, ChannelMixup, and other advanced strategies. I think these approaches can help improve some generalization and robustness .</p>",
      "rawMarkdown": "You're right — in this case, it would be ideal to leverage well-researched models like SSRN, SSATNet, and SugarViT, all of which have open-source implementations available on GitHub. In addition, we can enhance model performance through spectral augmentation techniques such as ChannelDropout, ChannelMixup, and other advanced strategies. I think these approaches can help improve some generalization and robustness .",
      "votes": null
    },
    {
      "id": "3203541",
      "postDate": "05/16/2025 22:29:23",
      "content": "<p>Thank you for sharing idia</p>",
      "rawMarkdown": "Thank you for sharing idia",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3193131,
      "author_name": "yantxx",
      "author_url": "",
      "post_date": "05/03/2025 22:57:18",
      "content": "<p>Yep… such a cool competition, but it seems there's a problem with how the data was extracted</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3193334,
      "author_name": "sarthak24910",
      "author_url": "",
      "post_date": "05/04/2025 08:22:54",
      "content": "<p>You're right — in this case, it would be ideal to leverage well-researched models like SSRN, SSATNet, and SugarViT, all of which have open-source implementations available on GitHub. In addition, we can enhance model performance through spectral augmentation techniques such as ChannelDropout, ChannelMixup, and other advanced strategies. I think these approaches can help improve some generalization and robustness .</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3203541,
      "author_name": "davidsunel",
      "author_url": "",
      "post_date": "05/16/2025 22:29:23",
      "content": "<p>Thank you for sharing idia</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3193068": "Hi! I’m really puzzled by the results. After a lot of experiments, it seems like the labels in the train file have no real significance-they feel almost random, and any attempt to actually model the data (even with domain-specific, agricultural approaches) just makes the score worse. In fact, just predicting the mean (around 49–50) for every patch seems to be the glass ceiling, and nothing I try can beat it.",
    "3193131": "Yep... such a cool competition, but it seems there's a problem with how the data was extracted",
    "3193334": "You're right — in this case, it would be ideal to leverage well-researched models like SSRN, SSATNet, and SugarViT, all of which have open-source implementations available on GitHub. In addition, we can enhance model performance through spectral augmentation techniques such as ChannelDropout, ChannelMixup, and other advanced strategies. I think these approaches can help improve some generalization and robustness .",
    "3203541": "Thank you for sharing idia"
  },
  "source": "meta"
}