{
  "id": 426797,
  "title": "How do I generate the submsission file?",
  "url": "/competitions/hubmap-hacking-the-human-vasculature/discussion/426797",
  "author_name": "Nair",
  "post_date": "2023-07-25T06:39:06.759000",
  "votes": 1,
  "comment_count": 3,
  "views": 0,
  "content": "<p>This is the first time that I am participating in a competition. I have completed the task, but I am unable to generate the proper submission file. Every file that I have been able to generate so far has been rejected. Can someone please look at it and tell me what I am doing wrong.</p>\n<p>`import cv2<br>\nimport os<br>\nimport pandas as pd<br>\nimport numpy as np<br>\nimport base64<br>\nimport zlib</p>\n<p>def rle_encode(img):</p>\n<pre><code>#  column wise\npixels = img..flatten()\npixels = np.concatenate([[], pixels, []])\nruns = np.where(pixels[:] != pixels[:-])[] + \nruns[::] -= runs[::]\nreturn .join(str(x) for x in runs)\n</code></pre>\n<p>test_dir = '/kaggle/input/hubmap-hacking-the-human-vasculature/test/'<br>\ntest_ids = os.listdir(test_dir)<br>\ntest_ids = [id_.split('.')[0] for id_ in test_ids if id_.endswith('.tif')]</p>\n<p>predictions = []</p>\n<p>for id_ in test_ids:<br>\n    # Load the image<br>\n    img_path = os.path.join(test_dir, f\"{id_}.tif\")<br>\n    img = cv2.imread(img_path, cv2.IMREAD_COLOR)<br>\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)</p>\n<pre><code>\n = (, )\n = cv2.resize(img, input_size)\n\n\n = img_resized / .\n\n = np.expand_dims(img_rescaled, axis=)\n\n = model.predict(img_input) &gt; .\n\n = np.squeeze(predicted_mask)\n\n len(predicted_mask.shape) == :\n     = np.max(predicted_mask, axis=-)\n\n\n = rle_encode(predicted_mask.astype(np.uint8))\n\n\n.append([id_, img.shape[], img.shape[], f' . {encoded_mask}'])\n</code></pre>\n<h1>Create the submission DataFrame</h1>\n<p>submission_df = pd.DataFrame(predictions, columns=['id', 'height', 'width', 'prediction_string'])</p>\n<h1>Save to csv file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)<br>\nprint(\"\\nsuccessfully saved submission file\\n\")`</p>",
  "messages": [
    {
      "id": 2357835,
      "postDate": "2023-07-25T06:39:06.760Z",
      "content": "<p>This is the first time that I am participating in a competition. I have completed the task, but I am unable to generate the proper submission file. Every file that I have been able to generate so far has been rejected. Can someone please look at it and tell me what I am doing wrong.</p>\n<p>`import cv2<br>\nimport os<br>\nimport pandas as pd<br>\nimport numpy as np<br>\nimport base64<br>\nimport zlib</p>\n<p>def rle_encode(img):</p>\n<pre><code>#  column wise\npixels = img..flatten()\npixels = np.concatenate([[], pixels, []])\nruns = np.where(pixels[:] != pixels[:-])[] + \nruns[::] -= runs[::]\nreturn .join(str(x) for x in runs)\n</code></pre>\n<p>test_dir = '/kaggle/input/hubmap-hacking-the-human-vasculature/test/'<br>\ntest_ids = os.listdir(test_dir)<br>\ntest_ids = [id_.split('.')[0] for id_ in test_ids if id_.endswith('.tif')]</p>\n<p>predictions = []</p>\n<p>for id_ in test_ids:<br>\n    # Load the image<br>\n    img_path = os.path.join(test_dir, f\"{id_}.tif\")<br>\n    img = cv2.imread(img_path, cv2.IMREAD_COLOR)<br>\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)</p>\n<pre><code>\n = (, )\n = cv2.resize(img, input_size)\n\n\n = img_resized / .\n\n = np.expand_dims(img_rescaled, axis=)\n\n = model.predict(img_input) &gt; .\n\n = np.squeeze(predicted_mask)\n\n len(predicted_mask.shape) == :\n     = np.max(predicted_mask, axis=-)\n\n\n = rle_encode(predicted_mask.astype(np.uint8))\n\n\n.append([id_, img.shape[], img.shape[], f' . {encoded_mask}'])\n</code></pre>\n<h1>Create the submission DataFrame</h1>\n<p>submission_df = pd.DataFrame(predictions, columns=['id', 'height', 'width', 'prediction_string'])</p>\n<h1>Save to csv file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)<br>\nprint(\"\\nsuccessfully saved submission file\\n\")`</p>",
      "rawMarkdown": "This is the first time that I am participating in a competition. I have completed the task, but I am unable to generate the proper submission file. Every file that I have been able to generate so far has been rejected. Can someone please look at it and tell me what I am doing wrong.\n\n`import cv2\nimport os\nimport pandas as pd\nimport numpy as np\nimport base64\nimport zlib\n\ndef rle_encode(img):\n \n    # Flatten column wise\n    pixels = img.T.flatten()\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\n\ntest_dir = '/kaggle/input/hubmap-hacking-the-human-vasculature/test/'\ntest_ids = os.listdir(test_dir)\ntest_ids = [id_.split('.')[0] for id_ in test_ids if id_.endswith('.tif')]\n\npredictions = []\n\nfor id_ in test_ids:\n    # Load the image\n    img_path = os.path.join(test_dir, f\"{id_}.tif\")\n    img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n    # Resize test image to match the input size of the model\n    input_size = (128, 128)\n    img_resized = cv2.resize(img, input_size)\n\n    # Rescale the pixel values to [0, 1] range\n    img_rescaled = img_resized / 255.\n\n    img_input = np.expand_dims(img_rescaled, axis=0)\n\n    predicted_mask = model.predict(img_input) > 0.5\n\n    predicted_mask = np.squeeze(predicted_mask)\n\n    if len(predicted_mask.shape) == 3:\n        predicted_mask = np.max(predicted_mask, axis=-1)\n\n    # Encode the mask\n    encoded_mask = rle_encode(predicted_mask.astype(np.uint8))\n    \n    # Save the id, size and mask\n    predictions.append([id_, img.shape[0], img.shape[1], f'0 1.0 {encoded_mask}'])\n\n# Create the submission DataFrame\nsubmission_df = pd.DataFrame(predictions, columns=['id', 'height', 'width', 'prediction_string'])\n\n# Save to csv file\nsubmission_df.to_csv('submission.csv', index=False)\nprint(\"\\nsuccessfully saved submission file\\n\")`",
      "votes": 1
    },
    {
      "id": 2361360,
      "postDate": "2023-07-27T11:04:46.430Z",
      "content": "<p>I would suggest modifying the kernels that was submitted successfully.<br>\n<a href=\"https://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation\" target=\"_blank\">https://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation</a><br>\n<a href=\"https://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference\" target=\"_blank\">https://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference</a><br>\n<a href=\"https://www.kaggle.com/code/itsuki9180/hubmap-inference\" target=\"_blank\">https://www.kaggle.com/code/itsuki9180/hubmap-inference</a><br>\n….</p>",
      "rawMarkdown": "\nI would suggest modifying the kernels that was submitted successfully.\n\nhttps://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation\n\nhttps://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference\n\nhttps://www.kaggle.com/code/itsuki9180/hubmap-inference\n\n....\n"
    },
    {
      "id": 2361253,
      "postDate": "2023-07-27T09:40:28.333Z",
      "content": "<p>You check your csv file is in the same format as sample_submission.csv file.<br>\nFirst column is id, second is height. third is width and fourth is prediction string.<br>\nYou may print the csv file and see if it matches or not. </p>",
      "rawMarkdown": "You check your csv file is in the same format as sample_submission.csv file.\nFirst column is id, second is height. third is width and fourth is prediction string.\nYou may print the csv file and see if it matches or not. \n"
    },
    {
      "id": 2358059,
      "postDate": "2023-07-25T09:21:11.033Z",
      "content": "<p>I'd be happy to help you with generating the proper submission file for a Kaggle competition. <br>\nIf you share more specific information about the competition, your log of code, I'll be able to provide more targeted assistance. </p>\n<p>The process generally involves the following steps:</p>\n<pre><code>       Understand  Submission Format: Before creating  submission , you need  understand  required  specified   competition organizers. Usually, this involves providing predictions   specific , such   CSV   particular column names  data types.\n\n       Make Predictions: You mentioned that you have completed  task, so I assume you have  machine learning model  some algorithm that generates predictions. Make sure your predictions are   correct   specified   competition.\n\n       Save Predictions  File: Use your programming language  choice (e.g., Python)  save  predictions   CSV . You can use libraries like Pandas  manipulate data  save    .\n\n       Check File Content: Verify  content   generated submission . Open     editor     your code  ensure  looks correct  matches  required .\n\n       Submit  :      ,      ,      ,         .\n\n       Verify Submission: After submitting, Kaggle will  your submission  evaluate  against  competitions status    .\n</code></pre>",
      "rawMarkdown": "I'd be happy to help you with generating the proper submission file for a Kaggle competition. \nIf you share more specific information about the competition, your log of code, I'll be able to provide more targeted assistance. \n\nThe process generally involves the following steps:\n\n          1. Understand the Submission Format: Before creating a submission file, you need to understand the required format specified by the competition organizers. Usually, this involves providing predictions in a specific format, such as a CSV file with particular column names and data types.\n\n          2. Make Predictions: You mentioned that you have completed the task, so I assume you have a machine learning model or some algorithm that generates predictions. Make sure your predictions are in the correct format as specified by the competition.\n\n          3. Save Predictions to File: Use your programming language of choice (e.g., Python) to save the predictions to a CSV file. You can use libraries like Pandas to manipulate data and save it to a file.\n\n          4. Check File Content: Verify the content of the generated submission file. Open it in a text editor or load it in your code to ensure it looks correct and matches the required format.\n\n          5. Submit on Kaggle: Once you have the submission file, go to the Kaggle competition page, find the \"Submit Predictions\" or similar button, and follow the instructions to upload your submission file.\n\n          6. Verify Submission: After submitting, Kaggle will process your submission and evaluate it against the competition's evaluation metric. You will receive a score, and you can check your submission's status on the competition leaderboard.",
      "isDeleted": true
    }
  ],
  "comments": [
    {
      "id": 2361360,
      "author_name": "atom1231",
      "author_url": "",
      "post_date": "2023-07-27T11:04:46.430000",
      "content": "<p>I would suggest modifying the kernels that was submitted successfully.<br>\n<a href=\"https://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation\" target=\"_blank\">https://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation</a><br>\n<a href=\"https://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference\" target=\"_blank\">https://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference</a><br>\n<a href=\"https://www.kaggle.com/code/itsuki9180/hubmap-inference\" target=\"_blank\">https://www.kaggle.com/code/itsuki9180/hubmap-inference</a><br>\n….</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 2361253,
      "author_name": "C R Suthikshn Kumar",
      "author_url": "",
      "post_date": "2023-07-27T09:40:28.333000",
      "content": "<p>You check your csv file is in the same format as sample_submission.csv file.<br>\nFirst column is id, second is height. third is width and fourth is prediction string.<br>\nYou may print the csv file and see if it matches or not. </p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 2358059,
      "author_name": "",
      "author_url": "",
      "post_date": "2023-07-25T09:21:11.033000",
      "content": "<p>I'd be happy to help you with generating the proper submission file for a Kaggle competition. <br>\nIf you share more specific information about the competition, your log of code, I'll be able to provide more targeted assistance. </p>\n<p>The process generally involves the following steps:</p>\n<pre><code>       Understand  Submission Format: Before creating  submission , you need  understand  required  specified   competition organizers. Usually, this involves providing predictions   specific , such   CSV   particular column names  data types.\n\n       Make Predictions: You mentioned that you have completed  task, so I assume you have  machine learning model  some algorithm that generates predictions. Make sure your predictions are   correct   specified   competition.\n\n       Save Predictions  File: Use your programming language  choice (e.g., Python)  save  predictions   CSV . You can use libraries like Pandas  manipulate data  save    .\n\n       Check File Content: Verify  content   generated submission . Open     editor     your code  ensure  looks correct  matches  required .\n\n       Submit  :      ,      ,      ,         .\n\n       Verify Submission: After submitting, Kaggle will  your submission  evaluate  against  competitions status    .\n</code></pre>",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2357835": "This is the first time that I am participating in a competition. I have completed the task, but I am unable to generate the proper submission file. Every file that I have been able to generate so far has been rejected. Can someone please look at it and tell me what I am doing wrong.\n\n`import cv2\nimport os\nimport pandas as pd\nimport numpy as np\nimport base64\nimport zlib\n\ndef rle_encode(img):\n \n    # Flatten column wise\n    pixels = img.T.flatten()\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\n\ntest_dir = '/kaggle/input/hubmap-hacking-the-human-vasculature/test/'\ntest_ids = os.listdir(test_dir)\ntest_ids = [id_.split('.')[0] for id_ in test_ids if id_.endswith('.tif')]\n\npredictions = []\n\nfor id_ in test_ids:\n    # Load the image\n    img_path = os.path.join(test_dir, f\"{id_}.tif\")\n    img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n    # Resize test image to match the input size of the model\n    input_size = (128, 128)\n    img_resized = cv2.resize(img, input_size)\n\n    # Rescale the pixel values to [0, 1] range\n    img_rescaled = img_resized / 255.\n\n    img_input = np.expand_dims(img_rescaled, axis=0)\n\n    predicted_mask = model.predict(img_input) > 0.5\n\n    predicted_mask = np.squeeze(predicted_mask)\n\n    if len(predicted_mask.shape) == 3:\n        predicted_mask = np.max(predicted_mask, axis=-1)\n\n    # Encode the mask\n    encoded_mask = rle_encode(predicted_mask.astype(np.uint8))\n    \n    # Save the id, size and mask\n    predictions.append([id_, img.shape[0], img.shape[1], f'0 1.0 {encoded_mask}'])\n\n# Create the submission DataFrame\nsubmission_df = pd.DataFrame(predictions, columns=['id', 'height', 'width', 'prediction_string'])\n\n# Save to csv file\nsubmission_df.to_csv('submission.csv', index=False)\nprint(\"\\nsuccessfully saved submission file\\n\")`",
    "2361360": "\nI would suggest modifying the kernels that was submitted successfully.\n\nhttps://www.kaggle.com/code/hidngnguyna/baseline-unet-semantic-as-instance-segmentation\n\nhttps://www.kaggle.com/code/atom1231/hubmap-mmdet-2-26-public-inference\n\nhttps://www.kaggle.com/code/itsuki9180/hubmap-inference\n\n....\n",
    "2361253": "You check your csv file is in the same format as sample_submission.csv file.\nFirst column is id, second is height. third is width and fourth is prediction string.\nYou may print the csv file and see if it matches or not. \n",
    "2358059": "I'd be happy to help you with generating the proper submission file for a Kaggle competition. \nIf you share more specific information about the competition, your log of code, I'll be able to provide more targeted assistance. \n\nThe process generally involves the following steps:\n\n          1. Understand the Submission Format: Before creating a submission file, you need to understand the required format specified by the competition organizers. Usually, this involves providing predictions in a specific format, such as a CSV file with particular column names and data types.\n\n          2. Make Predictions: You mentioned that you have completed the task, so I assume you have a machine learning model or some algorithm that generates predictions. Make sure your predictions are in the correct format as specified by the competition.\n\n          3. Save Predictions to File: Use your programming language of choice (e.g., Python) to save the predictions to a CSV file. You can use libraries like Pandas to manipulate data and save it to a file.\n\n          4. Check File Content: Verify the content of the generated submission file. Open it in a text editor or load it in your code to ensure it looks correct and matches the required format.\n\n          5. Submit on Kaggle: Once you have the submission file, go to the Kaggle competition page, find the \"Submit Predictions\" or similar button, and follow the instructions to upload your submission file.\n\n          6. Verify Submission: After submitting, Kaggle will process your submission and evaluate it against the competition's evaluation metric. You will receive a score, and you can check your submission's status on the competition leaderboard."
  }
}