{
  "id": 477648,
  "title": "DU_MetroRail Solution",
  "url": "/competitions/dl-enigma-10-sust-cse-carnival-2024/writeups/du-metrorail-du-metrorail-solution",
  "author_name": "",
  "post_date": "2024-02-18T08:26:16.103Z",
  "votes": 9,
  "comment_count": 4,
  "views": 0,
  "content": "<table>\n<thead>\n<tr>\n<th>Description</th>\n<th>Link</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>🚗🔍 <strong>Approach Summary</strong></td>\n<td>Our approach harnesses the power of the Co-DETR model 🤖 and the BadODD dataset 📊 for robust object detection on the intricate roads of Bangladesh, addressing unique challenges for autonomous navigation.</td>\n</tr>\n<tr>\n<td>📄 <strong>IEEE Format Paper</strong></td>\n<td><a href=\"https://drive.google.com/file/d/1iF3Q1592VmXjgrdZK8i0rxu5EOigeERc/view?usp=sharing\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🖥️ <strong>Approach Presentation</strong></td>\n<td><a href=\"https://docs.google.com/presentation/d/1rWYaTH43CLrU6WRhWF6Mi_S2PPLOfhUXHy4hoRJPv2U/edit?usp=drivesdk\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>📊 <strong>EDA Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-a-complete-eda\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔄 <strong>Data Augmentation Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-data-augmentation\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🖼️ <strong>Image Processing Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/istiaqahmedfahad/badodd-image-prerocessing\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔄 <strong>Dataset Conversion Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-yolo2coco\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>📓 <strong>Training Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/sakibbai/du-metrorail-training-notebook\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔍 <strong>Inference Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/sakibbai/du-metrorail-inference-notebook\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>💾 <strong>Model Weight</strong></td>\n<td><a href=\"https://www.kaggle.com/datasets/abdullaharean/du-metrorail-model\" target=\"_blank\">Link</a></td>\n</tr>\n</tbody>\n</table>",
  "messages": [
    {
      "id": "2655699",
      "postDate": "02/17/2024 06:42:10",
      "content": "<table>\n<thead>\n<tr>\n<th>Description</th>\n<th>Link</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>🚗🔍 <strong>Approach Summary</strong></td>\n<td>Our approach harnesses the power of the Co-DETR model 🤖 and the BadODD dataset 📊 for robust object detection on the intricate roads of Bangladesh, addressing unique challenges for autonomous navigation.</td>\n</tr>\n<tr>\n<td>📄 <strong>IEEE Format Paper</strong></td>\n<td><a href=\"https://drive.google.com/file/d/1iF3Q1592VmXjgrdZK8i0rxu5EOigeERc/view?usp=sharing\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🖥️ <strong>Approach Presentation</strong></td>\n<td><a href=\"https://docs.google.com/presentation/d/1rWYaTH43CLrU6WRhWF6Mi_S2PPLOfhUXHy4hoRJPv2U/edit?usp=drivesdk\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>📊 <strong>EDA Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-a-complete-eda\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔄 <strong>Data Augmentation Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-data-augmentation\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🖼️ <strong>Image Processing Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/istiaqahmedfahad/badodd-image-prerocessing\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔄 <strong>Dataset Conversion Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/abdullaharean/badodd-yolo2coco\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>📓 <strong>Training Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/sakibbai/du-metrorail-training-notebook\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>🔍 <strong>Inference Notebook</strong></td>\n<td><a href=\"https://www.kaggle.com/code/sakibbai/du-metrorail-inference-notebook\" target=\"_blank\">Link</a></td>\n</tr>\n<tr>\n<td>💾 <strong>Model Weight</strong></td>\n<td><a href=\"https://www.kaggle.com/datasets/abdullaharean/du-metrorail-model\" target=\"_blank\">Link</a></td>\n</tr>\n</tbody>\n</table>",
      "rawMarkdown": "| Description                           | Link                                                                                                   |\n|---------------------------------------|--------------------------------------------------------------------------------------------------------|\n| 🚗🔍 **Approach Summary**             | Our approach harnesses the power of the Co-DETR model 🤖 and the BadODD dataset 📊 for robust object detection on the intricate roads of Bangladesh, addressing unique challenges for autonomous navigation. |\n| 📄 **IEEE Format Paper**                    | [Link](https://drive.google.com/file/d/1iF3Q1592VmXjgrdZK8i0rxu5EOigeERc/view?usp=sharing)             |\n| 🖥️ **Approach Presentation**            | [Link](https://docs.google.com/presentation/d/1rWYaTH43CLrU6WRhWF6Mi_S2PPLOfhUXHy4hoRJPv2U/edit?usp=drivesdk) |\n| 📊 **EDA Notebook**                  | [Link](https://www.kaggle.com/code/abdullaharean/badodd-a-complete-eda)                                 |\n| 🔄 **Data Augmentation Notebook**    | [Link](https://www.kaggle.com/code/abdullaharean/badodd-data-augmentation)                              |\n| 🖼️ **Image Processing Notebook**    | [Link](https://www.kaggle.com/code/istiaqahmedfahad/badodd-image-prerocessing)                          |\n| 🔄 **Dataset Conversion Notebook**   | [Link](https://www.kaggle.com/code/abdullaharean/badodd-yolo2coco)                                       |\n| 📓 **Training Notebook**        | [Link](https://www.kaggle.com/sakibbai/du-metrorail-training-notebook)                                    |\n| 🔍 **Inference Notebook**       | [Link](https://www.kaggle.com/code/sakibbai/du-metrorail-inference-notebook)                              |\n| 💾 **Model Weight**             | [Link](https://www.kaggle.com/datasets/abdullaharean/du-metrorail-model)                                 |",
      "votes": null
    },
    {
      "id": "2655701",
      "postDate": "02/17/2024 06:46:25",
      "content": "<p>good approach and very intuitive solution… </p>",
      "rawMarkdown": "good approach and very intuitive solution...",
      "votes": null
    },
    {
      "id": "2655702",
      "postDate": "02/17/2024 06:47:26",
      "content": "<p>Thanks <a href=\"https://www.kaggle.com/tanishqdublish\" target=\"_blank\">@tanishqdublish</a> </p>",
      "rawMarkdown": "Thanks @tanishqdublish",
      "votes": null
    },
    {
      "id": "2663716",
      "postDate": "02/22/2024 16:44:23",
      "content": "<p>Got to learn a lot of new techniques. Thank you for sharing.</p>",
      "rawMarkdown": "Got to learn a lot of new techniques. Thank you for sharing.",
      "votes": null
    },
    {
      "id": "2664554",
      "postDate": "02/23/2024 05:46:19",
      "content": "<p>Thanks for your kind appreciation </p>",
      "rawMarkdown": "Thanks for your kind appreciation",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2655701,
      "author_name": "tanishqdublish",
      "author_url": "",
      "post_date": "02/17/2024 06:46:25",
      "content": "<p>good approach and very intuitive solution… </p>",
      "votes": null,
      "replies": [
        {
          "id": 2655702,
          "author_name": "abdullaharean",
          "author_url": "",
          "post_date": "02/17/2024 06:47:26",
          "content": "<p>Thanks <a href=\"https://www.kaggle.com/tanishqdublish\" target=\"_blank\">@tanishqdublish</a> </p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 2663716,
      "author_name": "killershoaib",
      "author_url": "",
      "post_date": "02/22/2024 16:44:23",
      "content": "<p>Got to learn a lot of new techniques. Thank you for sharing.</p>",
      "votes": null,
      "replies": [
        {
          "id": 2664554,
          "author_name": "abdullaharean",
          "author_url": "",
          "post_date": "02/23/2024 05:46:19",
          "content": "<p>Thanks for your kind appreciation </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2655699": "| Description                           | Link                                                                                                   |\n|---------------------------------------|--------------------------------------------------------------------------------------------------------|\n| 🚗🔍 **Approach Summary**             | Our approach harnesses the power of the Co-DETR model 🤖 and the BadODD dataset 📊 for robust object detection on the intricate roads of Bangladesh, addressing unique challenges for autonomous navigation. |\n| 📄 **IEEE Format Paper**                    | [Link](https://drive.google.com/file/d/1iF3Q1592VmXjgrdZK8i0rxu5EOigeERc/view?usp=sharing)             |\n| 🖥️ **Approach Presentation**            | [Link](https://docs.google.com/presentation/d/1rWYaTH43CLrU6WRhWF6Mi_S2PPLOfhUXHy4hoRJPv2U/edit?usp=drivesdk) |\n| 📊 **EDA Notebook**                  | [Link](https://www.kaggle.com/code/abdullaharean/badodd-a-complete-eda)                                 |\n| 🔄 **Data Augmentation Notebook**    | [Link](https://www.kaggle.com/code/abdullaharean/badodd-data-augmentation)                              |\n| 🖼️ **Image Processing Notebook**    | [Link](https://www.kaggle.com/code/istiaqahmedfahad/badodd-image-prerocessing)                          |\n| 🔄 **Dataset Conversion Notebook**   | [Link](https://www.kaggle.com/code/abdullaharean/badodd-yolo2coco)                                       |\n| 📓 **Training Notebook**        | [Link](https://www.kaggle.com/sakibbai/du-metrorail-training-notebook)                                    |\n| 🔍 **Inference Notebook**       | [Link](https://www.kaggle.com/code/sakibbai/du-metrorail-inference-notebook)                              |\n| 💾 **Model Weight**             | [Link](https://www.kaggle.com/datasets/abdullaharean/du-metrorail-model)                                 |",
    "2655701": "good approach and very intuitive solution...",
    "2655702": "Thanks @tanishqdublish",
    "2663716": "Got to learn a lot of new techniques. Thank you for sharing.",
    "2664554": "Thanks for your kind appreciation"
  },
  "source": "existing_discussion",
  "complete": true,
  "competition_id": 67836,
  "competition_slug": "dl-enigma-10-sust-cse-carnival-2024",
  "team_id": 11565123,
  "solution_url": "/competitions/dl-enigma-10-sust-cse-carnival-2024/writeups/du-metrorail-du-metrorail-solution"
}