{
  "id": 575620,
  "title": "Freezing the backbone layer in YOLO",
  "url": "/competitions/byu-locating-bacterial-flagellar-motors-2025/discussion/575620",
  "author_name": "",
  "post_date": "2025-04-29T21:50:18.444369300Z",
  "votes": 1,
  "comment_count": 1,
  "views": 0,
  "content": "<p>In YOLO, there are three main neural layers: <strong>backbone</strong>, <strong>neck</strong>, and <strong>head</strong>. At the initial stage, general features are learned. A common technique is to <strong>freeze the backbone layer</strong> for a number of epochs and then unfreeze it in the later stages. This approach helps to <strong>increase training speed</strong> and also <strong>preserve general features</strong> (such as edge detection and boundary recognition) that are often common across datasets like COCO.</p>",
  "messages": [
    {
      "id": "3189862",
      "postDate": "04/29/2025 21:50:18",
      "content": "<p>In YOLO, there are three main neural layers: <strong>backbone</strong>, <strong>neck</strong>, and <strong>head</strong>. At the initial stage, general features are learned. A common technique is to <strong>freeze the backbone layer</strong> for a number of epochs and then unfreeze it in the later stages. This approach helps to <strong>increase training speed</strong> and also <strong>preserve general features</strong> (such as edge detection and boundary recognition) that are often common across datasets like COCO.</p>",
      "rawMarkdown": "In YOLO, there are three main neural layers: **backbone**, **neck**, and **head**. At the initial stage, general features are learned. A common technique is to **freeze the backbone layer** for a number of epochs and then unfreeze it in the later stages. This approach helps to **increase training speed** and also **preserve general features** (such as edge detection and boundary recognition) that are often common across datasets like COCO.",
      "votes": null
    },
    {
      "id": "3190427",
      "postDate": "04/30/2025 16:53:57",
      "content": "<p>Yes this is called transfer learning.  Our dataset is wildly different from COCO and ImageNet so it is typically recommended to unfreeze more layers.</p>",
      "rawMarkdown": "Yes this is called transfer learning.  Our dataset is wildly different from COCO and ImageNet so it is typically recommended to unfreeze more layers.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3190427,
      "author_name": "connorjd",
      "author_url": "",
      "post_date": "04/30/2025 16:53:57",
      "content": "<p>Yes this is called transfer learning.  Our dataset is wildly different from COCO and ImageNet so it is typically recommended to unfreeze more layers.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3189862": "In YOLO, there are three main neural layers: **backbone**, **neck**, and **head**. At the initial stage, general features are learned. A common technique is to **freeze the backbone layer** for a number of epochs and then unfreeze it in the later stages. This approach helps to **increase training speed** and also **preserve general features** (such as edge detection and boundary recognition) that are often common across datasets like COCO.",
    "3190427": "Yes this is called transfer learning.  Our dataset is wildly different from COCO and ImageNet so it is typically recommended to unfreeze more layers."
  },
  "source": "meta"
}