{
  "id": 577949,
  "title": "Strip optimizer from epoch.pt in Ultralytics YOLO",
  "url": "/competitions/byu-locating-bacterial-flagellar-motors-2025/discussion/577949",
  "author_name": "min fuka",
  "post_date": "2025-05-07T23:49:21.721000",
  "votes": 26,
  "comment_count": 16,
  "views": 0,
  "content": "<p>The best.pt and last.pt created in the Ultralytics YOLO training are automatically optimized at the end of the training, but the other epoch.pt must be done manually.   <br>\nI have attached a batch optimization script for the Ultralytics model. It has been tested to work.    </p>\n<p>This was created by Manus. Manus appears to have been created from  <a href=\"https://github.com/ultralytics/ultralytics\" target=\"_blank\">https://github.com/ultralytics/ultralytics</a></p>\n<pre><code>\n\n\n argparse\n os\n datetime  datetime\n pathlib  Path\n\n torch\n\n\n ():\n    \n    :\n        \n        x = torch.load(f, map_location=torch.device())\n         (x, ), \n           x, \n     Exception  e:\n        ()\n         {}\n\n    \n    metadata = {\n        : datetime.now().isoformat(),\n        : ,\n    }\n\n    \n     x.get():\n        x[] = x[]  \n     (x[], ):\n        x[].args = (x[].args)  \n     (x[], ):\n        x[].criterion =   \n    x[].half()  \n     p  x[].parameters():\n        p.requires_grad =   \n\n    \n     k  [, , , ]:\n        x[k] = \n    x[] = -\n\n    \n    combined = {**metadata, **x, **(updates  {})}\n    torch.save(combined, s  f)\n\n    \n    mb = os.path.getsize(s  f) / \n    ()\n\n     combined\n\n\n ():\n    \n    parser = argparse.ArgumentParser(description=)\n    parser.add_argument(, =, required=, =)\n    parser.add_argument(, =, default=, =)\n    parser.add_argument(, action=, =)\n    args = parser.parse_args()\n\n    \n      os.path.isdir(args.):\n        ()\n        \n\n    \n    pt_files = (Path(args.).rglob())\n      pt_files:\n        ()\n        \n\n    ()\n\n    \n     pt_file  pt_files:\n         args.overwrite:\n            \n            strip_optimizer(pt_file)\n        :\n            \n            stem = pt_file.stem\n            output_file = pt_file.parent / \n            strip_optimizer(pt_file, (output_file))\n\n    ()\n\n\n __name__ == :\n    main()\n</code></pre>",
  "messages": [
    {
      "id": 3197231,
      "postDate": "2025-05-07T23:49:21.720Z",
      "content": "<p>The best.pt and last.pt created in the Ultralytics YOLO training are automatically optimized at the end of the training, but the other epoch.pt must be done manually.   <br>\nI have attached a batch optimization script for the Ultralytics model. It has been tested to work.    </p>\n<p>This was created by Manus. Manus appears to have been created from  <a href=\"https://github.com/ultralytics/ultralytics\" target=\"_blank\">https://github.com/ultralytics/ultralytics</a></p>\n<pre><code>\n\n\n argparse\n os\n datetime  datetime\n pathlib  Path\n\n torch\n\n\n ():\n    \n    :\n        \n        x = torch.load(f, map_location=torch.device())\n         (x, ), \n           x, \n     Exception  e:\n        ()\n         {}\n\n    \n    metadata = {\n        : datetime.now().isoformat(),\n        : ,\n    }\n\n    \n     x.get():\n        x[] = x[]  \n     (x[], ):\n        x[].args = (x[].args)  \n     (x[], ):\n        x[].criterion =   \n    x[].half()  \n     p  x[].parameters():\n        p.requires_grad =   \n\n    \n     k  [, , , ]:\n        x[k] = \n    x[] = -\n\n    \n    combined = {**metadata, **x, **(updates  {})}\n    torch.save(combined, s  f)\n\n    \n    mb = os.path.getsize(s  f) / \n    ()\n\n     combined\n\n\n ():\n    \n    parser = argparse.ArgumentParser(description=)\n    parser.add_argument(, =, required=, =)\n    parser.add_argument(, =, default=, =)\n    parser.add_argument(, action=, =)\n    args = parser.parse_args()\n\n    \n      os.path.isdir(args.):\n        ()\n        \n\n    \n    pt_files = (Path(args.).rglob())\n      pt_files:\n        ()\n        \n\n    ()\n\n    \n     pt_file  pt_files:\n         args.overwrite:\n            \n            strip_optimizer(pt_file)\n        :\n            \n            stem = pt_file.stem\n            output_file = pt_file.parent / \n            strip_optimizer(pt_file, (output_file))\n\n    ()\n\n\n __name__ == :\n    main()\n</code></pre>",
      "rawMarkdown": "The best.pt and last.pt created in the Ultralytics YOLO training are automatically optimized at the end of the training, but the other epoch.pt must be done manually.   \nI have attached a batch optimization script for the Ultralytics model. It has been tested to work.    \n\nThis was created by Manus. Manus appears to have been created from  https://github.com/ultralytics/ultralytics\n  \n\n\n\n    #!/usr/bin/env python3\n    \"\"\"\n    Batch optimization script for Ultralytics models\n    \n    This script batch optimizes all Ultralytics models (.pt files) in a specified directory.\n    It reduces file size by removing the optimizer and converting the model to half-precision (FP16).\n    \n    Usage:\n        python batch_optimize_models.py --dir /path/to/models/ [--suffix _optimized]\n    \n    Arguments:\n        --dir: Directory containing model files to optimize (required)\n        --suffix: Suffix to add to the output filename (optional, default is _optimized)\n        --overwrite: Specify this flag to overwrite input files (optional)\n    \"\"\"\n    \n    import argparse\n    import os\n    from datetime import datetime\n    from pathlib import Path\n    \n    import torch\n    \n    \n    def strip_optimizer(f, s=\"\", updates=None):\n        \"\"\"\n        Function to strip optimizer from a model to reduce its size.\n    \n        Args:\n            f (str | Path): Path to the model file to optimize.\n            s (str, optional): Path to save the optimized model. If not specified, overwrites the input file.\n            updates (dict, optional): Updates to add to the checkpoint.\n    \n        Returns:\n            dict: Updated checkpoint dictionary.\n        \"\"\"\n        try:\n            # Load model on CPU\n            x = torch.load(f, map_location=torch.device(\"cpu\"))\n            assert isinstance(x, dict), \"Checkpoint is not a Python dictionary\"\n            assert \"model\" in x, \"Checkpoint does not contain 'model'\"\n        except Exception as e:\n            print(f\"Error: {f} is not a valid Ultralytics model: {e}\")\n            return {}\n    \n        # Add metadata\n        metadata = {\n            \"date\": datetime.now().isoformat(),\n            \"optimized_by\": \"batch_optimize_models.py\",\n        }\n    \n        # Update model\n        if x.get(\"ema\"):\n            x[\"model\"] = x[\"ema\"]  # Replace with EMA model\n        if hasattr(x[\"model\"], \"args\"):\n            x[\"model\"].args = dict(x[\"model\"].args)  # Convert IterableSimpleNamespace to dict\n        if hasattr(x[\"model\"], \"criterion\"):\n            x[\"model\"].criterion = None  # Remove loss criterion\n        x[\"model\"].half()  # Convert to FP16\n        for p in x[\"model\"].parameters():\n            p.requires_grad = False  # Disable gradient calculation\n    \n        # Remove unnecessary keys\n        for k in [\"optimizer\", \"best_fitness\", \"ema\", \"updates\"]:\n            x[k] = None\n        x[\"epoch\"] = -1\n    \n        # Save\n        combined = {**metadata, **x, **(updates or {})}\n        torch.save(combined, s or f)\n        \n        # Calculate file size (in MB)\n        mb = os.path.getsize(s or f) / 1e6\n        print(f\"Optimizer stripped: {f}{f' -> {s}' if s else ''}, {mb:.1f}MB\")\n        \n        return combined\n    \n    \n    def main():\n        # Parse command line arguments\n        parser = argparse.ArgumentParser(description=\"Batch optimize Ultralytics models\")\n        parser.add_argument(\"--dir\", type=str, required=True, help=\"Directory containing model files to optimize\")\n        parser.add_argument(\"--suffix\", type=str, default=\"_optimized\", help=\"Suffix to add to the output filename (default: _optimized)\")\n        parser.add_argument(\"--overwrite\", action=\"store_true\", help=\"Specify this flag to overwrite input files\")\n        args = parser.parse_args()\n    \n        # Check if directory exists\n        if not os.path.isdir(args.dir):\n            print(f\"Error: Directory '{args.dir}' not found\")\n            return\n    \n        # Find .pt files\n        pt_files = list(Path(args.dir).rglob(\"*.pt\"))\n        if not pt_files:\n            print(f\"Warning: No .pt files found in directory '{args.dir}'\")\n            return\n    \n        print(f\"Processing {len(pt_files)} model files...\")\n    \n        # Optimize each model\n        for pt_file in pt_files:\n            if args.overwrite:\n                # Overwrite input file\n                strip_optimizer(pt_file)\n            else:\n                # Save with new filename\n                stem = pt_file.stem\n                output_file = pt_file.parent / f\"{stem}{args.suffix}{pt_file.suffix}\"\n                strip_optimizer(pt_file, str(output_file))\n    \n        print(\"Processing complete.\")\n    \n    \n    if __name__ == \"__main__\":\n        main()\n",
      "votes": 26
    },
    {
      "id": 3197271,
      "postDate": "2025-05-08T01:48:23.120Z",
      "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a>, thank you for sharing this. Would this affect the performance or file size only?</p>",
      "rawMarkdown": "@minfuka, thank you for sharing this. Would this affect the performance or file size only?",
      "votes": 1,
      "replies": [
        {
          "id": 3197273,
          "postDate": "2025-05-08T01:51:14.797Z",
          "content": "<p>The inference speed was slower on the model before optimization. I just didn't submit (too slow) so I don't know how much it affects the score.</p>",
          "rawMarkdown": "The inference speed was slower on the model before optimization. I just didn't submit (too slow) so I don't know how much it affects the score.",
          "votes": 2,
          "replies": [
            {
              "id": 3197286,
              "postDate": "2025-05-08T02:11:19.723Z",
              "content": "<p>Thank you. I tried both and let's see if there is a difference. </p>",
              "rawMarkdown": "Thank you. I tried both and let's see if there is a difference. ",
              "votes": 2
            },
            {
              "id": 3197750,
              "postDate": "2025-05-08T14:18:51.827Z",
              "content": "<p><a href=\"https://www.kaggle.com/hongweiluan\" target=\"_blank\">@hongweiluan</a> have you found a difference before and after?</p>",
              "rawMarkdown": "@hongweiluan have you found a difference before and after?"
            },
            {
              "id": 3197758,
              "postDate": "2025-05-08T14:38:30.683Z",
              "content": "<p>No, there is no difference. I got the same score on LB</p>",
              "rawMarkdown": "No, there is no difference. I got the same score on LB",
              "votes": 3
            },
            {
              "id": 3197965,
              "postDate": "2025-05-08T21:38:22.337Z",
              "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a> <br>\nFor your best_dfl_loss ckpt, could u provide some of the precision and recall scores? I just remembered an old experiment where the epoch36.pt gave me better results than best.pt. What was special about it is that it had almost equal precision and recall. It gave me much better results that best.pt</p>",
              "rawMarkdown": "@minfuka \nFor your best_dfl_loss ckpt, could u provide some of the precision and recall scores? I just remembered an old experiment where the epoch36.pt gave me better results than best.pt. What was special about it is that it had almost equal precision and recall. It gave me much better results that best.pt",
              "votes": 1
            },
            {
              "id": 3197988,
              "postDate": "2025-05-08T23:58:34.173Z",
              "content": "<p>Attached is a graph trained on yolo11L.<br>\nOnly the best.pt was saved for this model and it was LB0.537.<br>\nI changed the data slightly (not that much,difference of about 0.01-0.02 in LB:only best.pt in other experiments) and trained again with yolo11L and got LB0.746 for the best_dfl_loss model (difference 0.2 not 0.02!).<br>\nThe difference between best.pt and best_dfl_loss.pt is the combination of the original model and data, respectively. best.pt=best_dfl_loss.pt in some cases.<br>\nIn many cases (as far as my experiments are concerned), I believe that the best.pt epoch is in a state of overlearning in terms of dfl_loss.</p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8234844%2F74e3ef323f0e97a2ead98a8f4536a7c2%2F.png?generation=1746748633351366&amp;alt=media\" alt=\"\"></p>\n<p>Recall and mAP are used as an indication of how good the training is, but they are not linked to LB, so I haven't recorded it.</p>",
              "rawMarkdown": "Attached is a graph trained on yolo11L.\nOnly the best.pt was saved for this model and it was LB0.537.\nI changed the data slightly (not that much,difference of about 0.01-0.02 in LB:only best.pt in other experiments) and trained again with yolo11L and got LB0.746 for the best_dfl_loss model (difference 0.2 not 0.02!).\nThe difference between best.pt and best_dfl_loss.pt is the combination of the original model and data, respectively. best.pt=best_dfl_loss.pt in some cases.\nIn many cases (as far as my experiments are concerned), I believe that the best.pt epoch is in a state of overlearning in terms of dfl_loss.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8234844%2F74e3ef323f0e97a2ead98a8f4536a7c2%2F.png?generation=1746748633351366&alt=media)\n\nRecall and mAP are used as an indication of how good the training is, but they are not linked to LB, so I haven't recorded it.",
              "votes": 3
            },
            {
              "id": 3198073,
              "postDate": "2025-05-09T02:48:43.303Z",
              "content": "<p>you trained a underfit model and then fine tune it? isn't it similar to warm-up?</p>",
              "rawMarkdown": "you trained a underfit model and then fine tune it? isn't it similar to warm-up?"
            },
            {
              "id": 3198389,
              "postDate": "2025-05-09T11:04:45.303Z",
              "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a> Thanks for sharing this. It is really insightful. <br>\nIf you don't mind sharing, could i know your best lb for single yolo without external data? </p>",
              "rawMarkdown": "@minfuka Thanks for sharing this. It is really insightful. \nIf you don't mind sharing, could i know your best lb for single yolo without external data? "
            },
            {
              "id": 3198401,
              "postDate": "2025-05-09T11:35:01.363Z",
              "content": "<p>LB0.825 in best.pt if no external data, this is the value before trying best_dfl_loss.pt. I haven't tried it yet because I need to retrain to get best_dfl_loss.pt. Please let me keep yolo's version a secret.</p>",
              "rawMarkdown": "LB0.825 in best.pt if no external data, this is the value before trying best_dfl_loss.pt. I haven't tried it yet because I need to retrain to get best_dfl_loss.pt. Please let me keep yolo's version a secret.",
              "votes": 2
            }
          ]
        }
      ]
    },
    {
      "id": 3198907,
      "postDate": "2025-05-10T07:15:15.530Z",
      "content": "<p>its so complete,)</p>",
      "rawMarkdown": "its so complete,)"
    },
    {
      "id": 3198465,
      "postDate": "2025-05-09T13:09:38.960Z",
      "content": "<p>Do you try other model or only YoLO?</p>",
      "rawMarkdown": "Do you try other model or only YoLO?"
    },
    {
      "id": 3197240,
      "postDate": "2025-05-08T00:14:18.623Z",
      "content": "<p>thank very much<br>\nIt helps for me , cause im strugging the file size😂</p>",
      "rawMarkdown": "thank very much\nIt helps for me , cause im strugging the file size😂"
    },
    {
      "id": 3198558,
      "postDate": "2025-05-09T15:34:06.880Z",
      "rawMarkdown": "",
      "isDeleted": true,
      "replies": [
        {
          "id": 3198659,
          "postDate": "2025-05-09T18:08:42.573Z",
          "rawMarkdown": "",
          "isDeleted": true,
          "replies": [
            {
              "id": 3200086,
              "postDate": "2025-05-12T03:44:13.493Z",
              "rawMarkdown": "",
              "isDeleted": true
            }
          ]
        }
      ]
    }
  ],
  "comments": [
    {
      "id": 3197271,
      "author_name": "HongweiLuan",
      "author_url": "",
      "post_date": "2025-05-08T01:48:23.120000",
      "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a>, thank you for sharing this. Would this affect the performance or file size only?</p>",
      "votes": 1,
      "replies": [
        {
          "id": 3197273,
          "author_name": "min fuka",
          "author_url": "",
          "post_date": "2025-05-08T01:51:14.797000",
          "content": "<p>The inference speed was slower on the model before optimization. I just didn't submit (too slow) so I don't know how much it affects the score.</p>",
          "votes": 2,
          "replies": [
            {
              "id": 3197286,
              "author_name": "HongweiLuan",
              "author_url": "",
              "post_date": "2025-05-08T02:11:19.723000",
              "content": "<p>Thank you. I tried both and let's see if there is a difference. </p>",
              "votes": 2,
              "replies": []
            },
            {
              "id": 3197750,
              "author_name": "Mohamed Eltayeb",
              "author_url": "",
              "post_date": "2025-05-08T14:18:51.827000",
              "content": "<p><a href=\"https://www.kaggle.com/hongweiluan\" target=\"_blank\">@hongweiluan</a> have you found a difference before and after?</p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3197758,
              "author_name": "HongweiLuan",
              "author_url": "",
              "post_date": "2025-05-08T14:38:30.683000",
              "content": "<p>No, there is no difference. I got the same score on LB</p>",
              "votes": 3,
              "replies": []
            },
            {
              "id": 3197965,
              "author_name": "Mohamed Eltayeb",
              "author_url": "",
              "post_date": "2025-05-08T21:38:22.337000",
              "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a> <br>\nFor your best_dfl_loss ckpt, could u provide some of the precision and recall scores? I just remembered an old experiment where the epoch36.pt gave me better results than best.pt. What was special about it is that it had almost equal precision and recall. It gave me much better results that best.pt</p>",
              "votes": 1,
              "replies": []
            },
            {
              "id": 3197988,
              "author_name": "min fuka",
              "author_url": "",
              "post_date": "2025-05-08T23:58:34.173000",
              "content": "<p>Attached is a graph trained on yolo11L.<br>\nOnly the best.pt was saved for this model and it was LB0.537.<br>\nI changed the data slightly (not that much,difference of about 0.01-0.02 in LB:only best.pt in other experiments) and trained again with yolo11L and got LB0.746 for the best_dfl_loss model (difference 0.2 not 0.02!).<br>\nThe difference between best.pt and best_dfl_loss.pt is the combination of the original model and data, respectively. best.pt=best_dfl_loss.pt in some cases.<br>\nIn many cases (as far as my experiments are concerned), I believe that the best.pt epoch is in a state of overlearning in terms of dfl_loss.</p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8234844%2F74e3ef323f0e97a2ead98a8f4536a7c2%2F.png?generation=1746748633351366&amp;alt=media\" alt=\"\"></p>\n<p>Recall and mAP are used as an indication of how good the training is, but they are not linked to LB, so I haven't recorded it.</p>",
              "votes": 3,
              "replies": []
            },
            {
              "id": 3198073,
              "author_name": "MengYe",
              "author_url": "",
              "post_date": "2025-05-09T02:48:43.303000",
              "content": "<p>you trained a underfit model and then fine tune it? isn't it similar to warm-up?</p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3198389,
              "author_name": "Mohamed Eltayeb",
              "author_url": "",
              "post_date": "2025-05-09T11:04:45.303000",
              "content": "<p><a href=\"https://www.kaggle.com/minfuka\" target=\"_blank\">@minfuka</a> Thanks for sharing this. It is really insightful. <br>\nIf you don't mind sharing, could i know your best lb for single yolo without external data? </p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3198401,
              "author_name": "min fuka",
              "author_url": "",
              "post_date": "2025-05-09T11:35:01.363000",
              "content": "<p>LB0.825 in best.pt if no external data, this is the value before trying best_dfl_loss.pt. I haven't tried it yet because I need to retrain to get best_dfl_loss.pt. Please let me keep yolo's version a secret.</p>",
              "votes": 2,
              "replies": []
            }
          ]
        }
      ]
    },
    {
      "id": 3198907,
      "author_name": "Bettina33",
      "author_url": "",
      "post_date": "2025-05-10T07:15:15.530000",
      "content": "<p>its so complete,)</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3198465,
      "author_name": "Ernest Garcia PhD",
      "author_url": "",
      "post_date": "2025-05-09T13:09:38.960000",
      "content": "<p>Do you try other model or only YoLO?</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3197240,
      "author_name": "shiba-inu",
      "author_url": "",
      "post_date": "2025-05-08T00:14:18.623000",
      "content": "<p>thank very much<br>\nIt helps for me , cause im strugging the file size😂</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3198558,
      "author_name": "",
      "author_url": "",
      "post_date": "2025-05-09T15:34:06.880000",
      "content": "",
      "votes": 0,
      "replies": [
        {
          "id": 3198659,
          "author_name": "",
          "author_url": "",
          "post_date": "2025-05-09T18:08:42.573000",
          "content": "",
          "votes": 0,
          "replies": [
            {
              "id": 3200086,
              "author_name": "",
              "author_url": "",
              "post_date": "2025-05-12T03:44:13.493000",
              "content": "",
              "votes": 0,
              "replies": []
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3197231": "The best.pt and last.pt created in the Ultralytics YOLO training are automatically optimized at the end of the training, but the other epoch.pt must be done manually.   \nI have attached a batch optimization script for the Ultralytics model. It has been tested to work.    \n\nThis was created by Manus. Manus appears to have been created from  https://github.com/ultralytics/ultralytics\n  \n\n\n\n    #!/usr/bin/env python3\n    \"\"\"\n    Batch optimization script for Ultralytics models\n    \n    This script batch optimizes all Ultralytics models (.pt files) in a specified directory.\n    It reduces file size by removing the optimizer and converting the model to half-precision (FP16).\n    \n    Usage:\n        python batch_optimize_models.py --dir /path/to/models/ [--suffix _optimized]\n    \n    Arguments:\n        --dir: Directory containing model files to optimize (required)\n        --suffix: Suffix to add to the output filename (optional, default is _optimized)\n        --overwrite: Specify this flag to overwrite input files (optional)\n    \"\"\"\n    \n    import argparse\n    import os\n    from datetime import datetime\n    from pathlib import Path\n    \n    import torch\n    \n    \n    def strip_optimizer(f, s=\"\", updates=None):\n        \"\"\"\n        Function to strip optimizer from a model to reduce its size.\n    \n        Args:\n            f (str | Path): Path to the model file to optimize.\n            s (str, optional): Path to save the optimized model. If not specified, overwrites the input file.\n            updates (dict, optional): Updates to add to the checkpoint.\n    \n        Returns:\n            dict: Updated checkpoint dictionary.\n        \"\"\"\n        try:\n            # Load model on CPU\n            x = torch.load(f, map_location=torch.device(\"cpu\"))\n            assert isinstance(x, dict), \"Checkpoint is not a Python dictionary\"\n            assert \"model\" in x, \"Checkpoint does not contain 'model'\"\n        except Exception as e:\n            print(f\"Error: {f} is not a valid Ultralytics model: {e}\")\n            return {}\n    \n        # Add metadata\n        metadata = {\n            \"date\": datetime.now().isoformat(),\n            \"optimized_by\": \"batch_optimize_models.py\",\n        }\n    \n        # Update model\n        if x.get(\"ema\"):\n            x[\"model\"] = x[\"ema\"]  # Replace with EMA model\n        if hasattr(x[\"model\"], \"args\"):\n            x[\"model\"].args = dict(x[\"model\"].args)  # Convert IterableSimpleNamespace to dict\n        if hasattr(x[\"model\"], \"criterion\"):\n            x[\"model\"].criterion = None  # Remove loss criterion\n        x[\"model\"].half()  # Convert to FP16\n        for p in x[\"model\"].parameters():\n            p.requires_grad = False  # Disable gradient calculation\n    \n        # Remove unnecessary keys\n        for k in [\"optimizer\", \"best_fitness\", \"ema\", \"updates\"]:\n            x[k] = None\n        x[\"epoch\"] = -1\n    \n        # Save\n        combined = {**metadata, **x, **(updates or {})}\n        torch.save(combined, s or f)\n        \n        # Calculate file size (in MB)\n        mb = os.path.getsize(s or f) / 1e6\n        print(f\"Optimizer stripped: {f}{f' -> {s}' if s else ''}, {mb:.1f}MB\")\n        \n        return combined\n    \n    \n    def main():\n        # Parse command line arguments\n        parser = argparse.ArgumentParser(description=\"Batch optimize Ultralytics models\")\n        parser.add_argument(\"--dir\", type=str, required=True, help=\"Directory containing model files to optimize\")\n        parser.add_argument(\"--suffix\", type=str, default=\"_optimized\", help=\"Suffix to add to the output filename (default: _optimized)\")\n        parser.add_argument(\"--overwrite\", action=\"store_true\", help=\"Specify this flag to overwrite input files\")\n        args = parser.parse_args()\n    \n        # Check if directory exists\n        if not os.path.isdir(args.dir):\n            print(f\"Error: Directory '{args.dir}' not found\")\n            return\n    \n        # Find .pt files\n        pt_files = list(Path(args.dir).rglob(\"*.pt\"))\n        if not pt_files:\n            print(f\"Warning: No .pt files found in directory '{args.dir}'\")\n            return\n    \n        print(f\"Processing {len(pt_files)} model files...\")\n    \n        # Optimize each model\n        for pt_file in pt_files:\n            if args.overwrite:\n                # Overwrite input file\n                strip_optimizer(pt_file)\n            else:\n                # Save with new filename\n                stem = pt_file.stem\n                output_file = pt_file.parent / f\"{stem}{args.suffix}{pt_file.suffix}\"\n                strip_optimizer(pt_file, str(output_file))\n    \n        print(\"Processing complete.\")\n    \n    \n    if __name__ == \"__main__\":\n        main()\n",
    "3197271": "@minfuka, thank you for sharing this. Would this affect the performance or file size only?",
    "3198907": "its so complete,)",
    "3198465": "Do you try other model or only YoLO?",
    "3197240": "thank very much\nIt helps for me , cause im strugging the file size😂",
    "3198558": ""
  }
}