{
  "id": 590203,
  "title": "why use seblock with sensor data?",
  "url": "/competitions/cmi-detect-behavior-with-sensor-data/discussion/590203",
  "author_name": "",
  "post_date": "2025-07-18T16:21:19.505076600Z",
  "votes": 3,
  "comment_count": 2,
  "views": 0,
  "content": "<p>I saw that a lot of the shared code uses SEBlock, which I wasn’t familiar with before. After some research I learned that it's designed to help the model identify which channels are more important than others. From what I found it seems to be commonly used in image data. That got me wondering how does SEBlock help when working with sensor data like in this competition? What exactly are the \"channels\" in this context? In image data it's clear (e.g., RGB channels), but what does it correspond to in sensor data?</p>",
  "messages": [
    {
      "id": "3250547",
      "postDate": "07/18/2025 16:21:19",
      "content": "<p>I saw that a lot of the shared code uses SEBlock, which I wasn’t familiar with before. After some research I learned that it's designed to help the model identify which channels are more important than others. From what I found it seems to be commonly used in image data. That got me wondering how does SEBlock help when working with sensor data like in this competition? What exactly are the \"channels\" in this context? In image data it's clear (e.g., RGB channels), but what does it correspond to in sensor data?</p>",
      "rawMarkdown": "I saw that a lot of the shared code uses SEBlock, which I wasn’t familiar with before. After some research I learned that it's designed to help the model identify which channels are more important than others. From what I found it seems to be commonly used in image data. That got me wondering how does SEBlock help when working with sensor data like in this competition? What exactly are the \"channels\" in this context? In image data it's clear (e.g., RGB channels), but what does it correspond to in sensor data?",
      "votes": null
    },
    {
      "id": "3250585",
      "postDate": "07/18/2025 18:15:05",
      "content": "<p>channels= features</p>",
      "rawMarkdown": "channels= features",
      "votes": null
    },
    {
      "id": "3250817",
      "postDate": "07/19/2025 07:52:57",
      "content": "<p>each sensor feature <code>acc_[x/y/z], rot_[w/x/y/z], thm_[1-5], tof_[1-5]_v[0-63]</code> can seen as separate channel</p>",
      "rawMarkdown": "each sensor feature `acc_[x/y/z], rot_[w/x/y/z], thm_[1-5], tof_[1-5]_v[0-63]` can seen as separate channel",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3250585,
      "author_name": "tahaalshatiri",
      "author_url": "",
      "post_date": "07/18/2025 18:15:05",
      "content": "<p>channels= features</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3250817,
      "author_name": "steubk",
      "author_url": "",
      "post_date": "07/19/2025 07:52:57",
      "content": "<p>each sensor feature <code>acc_[x/y/z], rot_[w/x/y/z], thm_[1-5], tof_[1-5]_v[0-63]</code> can seen as separate channel</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3250547": "I saw that a lot of the shared code uses SEBlock, which I wasn’t familiar with before. After some research I learned that it's designed to help the model identify which channels are more important than others. From what I found it seems to be commonly used in image data. That got me wondering how does SEBlock help when working with sensor data like in this competition? What exactly are the \"channels\" in this context? In image data it's clear (e.g., RGB channels), but what does it correspond to in sensor data?",
    "3250585": "channels= features",
    "3250817": "each sensor feature `acc_[x/y/z], rot_[w/x/y/z], thm_[1-5], tof_[1-5]_v[0-63]` can seen as separate channel"
  },
  "source": "meta"
}