{
  "id": 527305,
  "title": "How to Reduce Loss further?",
  "url": "/competitions/rsna-2024-lumbar-spine-degenerative-classification/discussion/527305",
  "author_name": "",
  "post_date": "2024-08-11T14:02:56.986891700Z",
  "votes": null,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Pre-trained single CNN models achieve a performance threshold of approximately 0.57-0.60 on this dataset. I tested many of them. What strategies can be employed to enhance the model's accuracy and reduce the loss? I would appreciate any insights or recommendations. Thank you.</p>",
  "messages": [
    {
      "id": "2955774",
      "postDate": "08/11/2024 14:02:56",
      "content": "<p>Pre-trained single CNN models achieve a performance threshold of approximately 0.57-0.60 on this dataset. I tested many of them. What strategies can be employed to enhance the model's accuracy and reduce the loss? I would appreciate any insights or recommendations. Thank you.</p>",
      "rawMarkdown": "Pre-trained single CNN models achieve a performance threshold of approximately 0.57-0.60 on this dataset. I tested many of them. What strategies can be employed to enhance the model's accuracy and reduce the loss? I would appreciate any insights or recommendations. Thank you.",
      "votes": null
    },
    {
      "id": "2956356",
      "postDate": "08/12/2024 02:12:28",
      "content": "<p>Have you tried mixup or related techniques </p>",
      "rawMarkdown": "Have you tried mixup or related techniques",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2956356,
      "author_name": "samu2505",
      "author_url": "",
      "post_date": "08/12/2024 02:12:28",
      "content": "<p>Have you tried mixup or related techniques </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2955774": "Pre-trained single CNN models achieve a performance threshold of approximately 0.57-0.60 on this dataset. I tested many of them. What strategies can be employed to enhance the model's accuracy and reduce the loss? I would appreciate any insights or recommendations. Thank you.",
    "2956356": "Have you tried mixup or related techniques"
  },
  "source": "meta"
}