{
  "total": 58,
  "topics": [
    {
      "id": 510006,
      "title": "Congratulations and Collaboration!!!!",
      "comment_count": 2,
      "views": 0,
      "votes": 2
    },
    {
      "id": 473713,
      "title": "New to Kaggle or Machine Learning? Check this out ~",
      "comment_count": 5,
      "views": 0,
      "votes": 8
    },
    {
      "id": 486503,
      "title": "Updated test dataset and rescoring",
      "comment_count": 13,
      "views": 0,
      "votes": 13
    },
    {
      "id": 483414,
      "title": "Updated Test Dataset",
      "comment_count": 8,
      "views": 0,
      "votes": 11
    },
    {
      "id": 480278,
      "title": "This Competition has an Official Discord Channel",
      "comment_count": 0,
      "views": 0,
      "votes": 6
    },
    {
      "id": 473714,
      "title": "Looking for a Team Megathread",
      "comment_count": 11,
      "views": 0,
      "votes": 5
    },
    {
      "id": 510393,
      "title": "1st Place Solution - PlantHydra 🐍",
      "comment_count": 13,
      "views": 0,
      "votes": 29
    },
    {
      "id": 512405,
      "title": "Share Your Single Model Scores: Let's See How Far Our Individual Models Went! 🌱",
      "comment_count": 0,
      "views": 0,
      "votes": 2
    },
    {
      "id": 510143,
      "title": "6th Place Solution with AutoML☘️",
      "comment_count": 4,
      "views": 0,
      "votes": 9
    },
    {
      "id": 510188,
      "title": "9th place solution - DINOv2 + CatBoost = 🚀 ",
      "comment_count": 1,
      "views": 0,
      "votes": 9
    },
    {
      "id": 510201,
      "title": "Late submissions",
      "comment_count": 1,
      "views": 0,
      "votes": 0
    },
    {
      "id": 509830,
      "title": "interesting scores on the private leaderboard 🍀",
      "comment_count": 4,
      "views": 0,
      "votes": null
    },
    {
      "id": 500513,
      "title": "👀 [Solved] About Timeline🌱",
      "comment_count": 9,
      "views": 0,
      "votes": 3
    },
    {
      "id": 505552,
      "title": "Invalid number of submission columns. Found 7",
      "comment_count": 4,
      "views": 0,
      "votes": 1
    },
    {
      "id": 490743,
      "title": "👀 Improved prediction accuracy of X50 🍄",
      "comment_count": 5,
      "views": 0,
      "votes": 5
    },
    {
      "id": 494941,
      "title": "Clarifying the Prediction Target: X[*]_mean vs. X[*]",
      "comment_count": 6,
      "views": 0,
      "votes": 3
    },
    {
      "id": 494547,
      "title": "Negative X4 Values in Dataset",
      "comment_count": 3,
      "views": 0,
      "votes": 2
    },
    {
      "id": 496591,
      "title": "Migrating from playground Competitions to this seems overwhelming",
      "comment_count": 4,
      "views": 0,
      "votes": 7
    },
    {
      "id": 494618,
      "title": "Target units for traits",
      "comment_count": 1,
      "views": 0,
      "votes": 2
    },
    {
      "id": 494550,
      "title": "👀 To enhance model performance 🚀",
      "comment_count": 3,
      "views": 0,
      "votes": 1
    },
    {
      "id": 493236,
      "title": "👀 Take a close look at train_images🌲",
      "comment_count": 1,
      "views": 0,
      "votes": 7
    },
    {
      "id": 487985,
      "title": "Column order for submission matters!",
      "comment_count": 3,
      "views": 0,
      "votes": 13
    },
    {
      "id": 492488,
      "title": "Tabular+Images -> CNN -> Multi-Target Regression",
      "comment_count": 1,
      "views": 0,
      "votes": 5
    },
    {
      "id": 485844,
      "title": "Why not provide the plant species?",
      "comment_count": 4,
      "views": 0,
      "votes": 7
    },
    {
      "id": 485354,
      "title": "NaNs in SD train targets?",
      "comment_count": 4,
      "views": 0,
      "votes": 1
    },
    {
      "id": 489515,
      "title": "Tabular -> ImageNet for Multi-Target Regression (.22 LB so far)",
      "comment_count": 0,
      "views": 0,
      "votes": 4
    },
    {
      "id": 487179,
      "title": "Add image features to tabular data - easy .2180 LB",
      "comment_count": 2,
      "views": 0,
      "votes": 7
    },
    {
      "id": 487904,
      "title": "👀 CV vs LB - Updated test dataset -🍀",
      "comment_count": 0,
      "views": 0,
      "votes": 2
    },
    {
      "id": 487367,
      "title": "local test set r2score completely different from kaggle test set r2score ",
      "comment_count": 2,
      "views": 0,
      "votes": 1
    },
    {
      "id": 486973,
      "title": "Efficientnetb0 only for positive score (+0.08921)",
      "comment_count": 0,
      "views": 0,
      "votes": 4
    },
    {
      "id": 486021,
      "title": "Do we normalize/scale the labels?",
      "comment_count": 5,
      "views": 0,
      "votes": 1
    },
    {
      "id": 484246,
      "title": "Units of targets",
      "comment_count": 3,
      "views": 0,
      "votes": 1
    },
    {
      "id": 486950,
      "title": "How to Improve the Model With the Revised Data?",
      "comment_count": 0,
      "views": 0,
      "votes": 1
    },
    {
      "id": 486094,
      "title": "👀 Impact of outliers on R2 🌿",
      "comment_count": 4,
      "views": 0,
      "votes": 2
    },
    {
      "id": 485970,
      "title": "Other competitions at the FGVC11 Workshop [CVPR] ",
      "comment_count": 0,
      "views": 0,
      "votes": 3
    },
    {
      "id": 484732,
      "title": "Need some explnantion regarding the scope of this project (I am new to Kaggle competition)",
      "comment_count": 5,
      "views": 0,
      "votes": 4
    },
    {
      "id": 483574,
      "title": "Sample_submission + tabular model = .3584 LB",
      "comment_count": 2,
      "views": 0,
      "votes": 2
    },
    {
      "id": 483259,
      "title": "Is this a valid competition after all?",
      "comment_count": 10,
      "views": 0,
      "votes": 10
    },
    {
      "id": 480563,
      "title": "Tabular only for +0.02486",
      "comment_count": 3,
      "views": 0,
      "votes": 4
    },
    {
      "id": 483990,
      "title": "sample_submission problem",
      "comment_count": 4,
      "views": 0,
      "votes": 6
    },
    {
      "id": 481301,
      "title": "Plant Trait Visualizer",
      "comment_count": 2,
      "views": 0,
      "votes": 4
    },
    {
      "id": 483518,
      "title": "sample_submission.csv now -33.38 on LB (with new test data)",
      "comment_count": 0,
      "views": 0,
      "votes": 2
    },
    {
      "id": 478835,
      "title": "Question on traits measurements",
      "comment_count": 3,
      "views": 0,
      "votes": 8
    },
    {
      "id": 479067,
      "title": "Better CV, lower LB？",
      "comment_count": 7,
      "views": 0,
      "votes": 3
    },
    {
      "id": 481726,
      "title": "Poor labeling",
      "comment_count": 2,
      "views": 0,
      "votes": 7
    },
    {
      "id": 478549,
      "title": "How To Handle Extreme Label Values",
      "comment_count": 10,
      "views": 0,
      "votes": 9
    },
    {
      "id": 482433,
      "title": "welwitschia mirabilis : correct or incorrect labeled",
      "comment_count": 1,
      "views": 0,
      "votes": 3
    },
    {
      "id": 481830,
      "title": "Outlier Visualization (a clue?)",
      "comment_count": 0,
      "views": 0,
      "votes": 4
    },
    {
      "id": 481778,
      "title": "MixUp augmentation",
      "comment_count": 0,
      "views": 0,
      "votes": 2
    },
    {
      "id": 481303,
      "title": "Does one outlier make R2 meaningless on numerically large targets?",
      "comment_count": 5,
      "views": 0,
      "votes": 4
    },
    {
      "id": 473745,
      "title": "Machine Vision for Plant Phenotyping.  Segmentation Techniques. Citizen Science. ",
      "comment_count": 13,
      "views": 0,
      "votes": 37
    },
    {
      "id": 481308,
      "title": "\"The R2 can result in large negative values. To prevent that we will only consider R2 values > 0.\"",
      "comment_count": 1,
      "views": 0,
      "votes": 3
    },
    {
      "id": 478544,
      "title": "Prediction Column Mean Does Not Result In R2 Of 0",
      "comment_count": 1,
      "views": 0,
      "votes": 4
    },
    {
      "id": 477883,
      "title": "🖥️📚PlantTraits Data Exploration!📊🔍",
      "comment_count": 2,
      "views": 0,
      "votes": 2
    },
    {
      "id": 476520,
      "title": "Learning Image Processing and Computer Vision ML",
      "comment_count": 2,
      "views": 0,
      "votes": 3
    },
    {
      "id": 476553,
      "title": "Plant species",
      "comment_count": 2,
      "views": 0,
      "votes": 4
    },
    {
      "id": 475370,
      "title": "Number of plant traits still 6 - while proposed aggregate dataset is supposed to have 33 of them.",
      "comment_count": 4,
      "views": 0,
      "votes": 5
    },
    {
      "id": 473764,
      "title": " Plants from Photos: Deep Learning & Citizen Science ... according to Bard",
      "comment_count": 1,
      "views": 0,
      "votes": 6
    }
  ],
  "errors": []
}