![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F26368162%2F2db39e6e9c6f9970bbd009dc7b5d7855%2FCapture%20dcran%202025-11-11%20115056.png?generation=1762858283093713&alt=media\")
It’s possible that the test set includes samples with slightly or even noticeably different textures, lighting conditions, or noise characteristics, which can make segmentation more difficult for most models.
\nThe evaluation metric itself is also extremely demanding, it’s not about detecting the general region, but achieving pixel-level precision.
\nThe scoring is based on F1 matching between predicted and ground-truth masks, using the Hungarian algorithm to optimally align them:
\ndef calculate_f1_score(pred_mask, gt_mask):\n tp = np.sum((pred_mask == 1) & (gt_mask == 1))\n fp = np.sum((pred_mask == 1) & (gt_mask == 0))\n fn = np.sum((pred_mask == 0) & (gt_mask == 1))\n precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n return 2 * precision * recall / (precision + recall + 1e-6)\n
Because of this, even small spatial misalignments or imperfect mask boundaries can significantly reduce the score, the metric rewards exact overlap rather than approximate localization.
\nThis explains why even strong architectures such as U-Net, YOLO, or R-CNN often struggle here:\nthey can identify forged regions in general, but not always with the pixel-perfect alignment that the metric expects.
\nSo the bottleneck is rarely in the inference or RLE code, it lies in the model’s ability to capture fine-grained local boundaries and texture consistency.\nReaching higher scores will likely require models capable of producing sharper, structurally consistent masks.
\nOne possible direction is introducing contextual or structural refinement mechanisms (e.g. patch-level refinement, edge-aware regularization).\nIt’s not just about applying standard segmentation architectures.
\n💡it’s about developing a more robust, geometry-aware approach that understands the structure of scientific textures.
\n💬** If anyone has additional insights or observations about this, feel free to share them here, it would be great to exchange perspectives and deepen our collective understanding of this challenge.**
", "messages": [ { "id": "3313150", "postDate": "11/08/2025 18:42:06", "content": "It’s possible that the test set includes samples with slightly or even noticeably different textures, lighting conditions, or noise characteristics, which can make segmentation more difficult for most models.
\nThe evaluation metric itself is also extremely demanding, it’s not about detecting the general region, but achieving pixel-level precision.
\nThe scoring is based on F1 matching between predicted and ground-truth masks, using the Hungarian algorithm to optimally align them:
\ndef calculate_f1_score(pred_mask, gt_mask):\n tp = np.sum((pred_mask == 1) & (gt_mask == 1))\n fp = np.sum((pred_mask == 1) & (gt_mask == 0))\n fn = np.sum((pred_mask == 0) & (gt_mask == 1))\n precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n return 2 * precision * recall / (precision + recall + 1e-6)\n
Because of this, even small spatial misalignments or imperfect mask boundaries can significantly reduce the score, the metric rewards exact overlap rather than approximate localization.
\nThis explains why even strong architectures such as U-Net, YOLO, or R-CNN often struggle here:\nthey can identify forged regions in general, but not always with the pixel-perfect alignment that the metric expects.
\nSo the bottleneck is rarely in the inference or RLE code, it lies in the model’s ability to capture fine-grained local boundaries and texture consistency.\nReaching higher scores will likely require models capable of producing sharper, structurally consistent masks.
\nOne possible direction is introducing contextual or structural refinement mechanisms (e.g. patch-level refinement, edge-aware regularization).\nIt’s not just about applying standard segmentation architectures.
\n💡it’s about developing a more robust, geometry-aware approach that understands the structure of scientific textures.
\n💬** If anyone has additional insights or observations about this, feel free to share them here, it would be great to exchange perspectives and deepen our collective understanding of this challenge.**
", "rawMarkdown": "It’s possible that the test set includes samples with slightly or even noticeably different textures, lighting conditions, or noise characteristics, which can make segmentation more difficult for most models.\n\nThe evaluation metric itself is also extremely demanding, it’s not about detecting the general region, but achieving pixel-level precision.\n\nThe scoring is based on F1 matching between predicted and ground-truth masks, using the Hungarian algorithm to optimally align them:\n\n `def calculate_f1_score(pred_mask, gt_mask):\n tp = np.sum((pred_mask == 1) & (gt_mask == 1))\n fp = np.sum((pred_mask == 1) & (gt_mask == 0))\n fn = np.sum((pred_mask == 0) & (gt_mask == 1))\n precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n return 2 * precision * recall / (precision + recall + 1e-6)\n`\n\nBecause of this, even small spatial misalignments or imperfect mask boundaries can significantly reduce the score, the metric rewards exact overlap rather than approximate localization.\n\nThis explains why even strong architectures such as U-Net, YOLO, or R-CNN often struggle here:\nthey can identify forged regions in general, but not always with the pixel-perfect alignment that the metric expects.\n\nSo the bottleneck is rarely in the inference or RLE code, it lies in the model’s ability to capture fine-grained local boundaries and texture consistency.\nReaching higher scores will likely require models capable of producing sharper, structurally consistent masks.\n\nOne possible direction is introducing contextual or structural refinement mechanisms (e.g. patch-level refinement, edge-aware regularization).\nIt’s not just about applying standard segmentation architectures. \n\n💡it’s about developing a more robust, geometry-aware approach that understands the structure of scientific textures.\n\n💬** If anyone has additional insights or observations about this, feel free to share them here, it would be great to exchange perspectives and deepen our collective understanding of this challenge.**", "votes": null }, { "id": "3313178", "postDate": "11/08/2025 20:51:13", "content": "Hi,\nThere is another point: the images haven't the same channels, some of them are in RGB format, another are in RGBA… It´s complicated but it's very funny 😊\nWe have a lot of time and we can enjoy playing with data and models
", "rawMarkdown": "Hi,\nThere is another point: the images haven't the same channels, some of them are in RGB format, another are in RGBA... It´s complicated but it's very funny 😊\nWe have a lot of time and we can enjoy playing with data and models", "votes": null }, { "id": "3314098", "postDate": "11/10/2025 04:36:00", "content": "Hey sister how are you my score is not increasing from 0.305 whats the issue please help me
", "rawMarkdown": "Hey sister how are you my score is not increasing from 0.305 whats the issue please help me", "votes": null }, { "id": "3314129", "postDate": "11/10/2025 04:59:06", "content": "Hey! I’m stuck like you 😅 \nTry using these parameters:
\nhey sister i use you notebook thanks for that you public your notebook but my score is not increasing
", "rawMarkdown": "hey sister i use you notebook thanks for that you public your notebook but my score is not increasing", "votes": null }, { "id": "3315183", "postDate": "11/10/2025 14:05:58", "content": "Hey! I applied the parameters below in the public notebook, and it gave me a small boost (0.307). You can try them or further develop the initial solution.
", "rawMarkdown": "Hey! I applied the parameters below in the public notebook, and it gave me a small boost (0.307). You can try them or further develop the initial solution.", "votes": null }, { "id": "3318288", "postDate": "11/11/2025 05:08:13", "content": "oh thanks sister
", "rawMarkdown": "oh thanks sister", "votes": null }, { "id": "3318289", "postDate": "11/11/2025 05:08:31", "content": "always live happy
", "rawMarkdown": "always live happy", "votes": null }, { "id": "3318740", "postDate": "11/11/2025 10:52:39", "content": "
I also found it hard that some cells are labeled with boxes and not by cell shape. It lost almost half of the F1 points because of that :/
", "rawMarkdown": "\n\nI also found it hard that some cells are labeled with boxes and not by cell shape. It lost almost half of the F1 points because of that :/", "votes": null }, { "id": "3319027", "postDate": "11/11/2025 14:23:07", "content": "Your observation is keen. Especially when it comes to stripes with unclear boundaries, which are often masked with rectangular shapes, this poses a challenging problem that needs to be addressed.
", "rawMarkdown": "Your observation is keen. Especially when it comes to stripes with unclear boundaries, which are often masked with rectangular shapes, this poses a challenging problem that needs to be addressed.", "votes": null }, { "id": "3324557", "postDate": "11/15/2025 01:50:01", "content": "hey sister i am trying from few days but my score is not increasing what i need to do
", "rawMarkdown": "hey sister i am trying from few days but my score is not increasing what i need to do", "votes": null }, { "id": "3325975", "postDate": "11/15/2025 08:14:08", "content": "I completely agree with your observations about the difficulty of this challenge- pixel-level F1, varying textures, lighting, and even some rectangular GT masks make it really tricky.
\nOne additional point that is crucial is the extreme asymmetry between forged and authentic pixels: forged pixels are extremely rare, so if the model isn’t trained carefully, it tends to collapse and predict “authentic everywhere,” which limits the score to around 0.30–0.32.
\nIn my experience, a staged training approach can help solve both problems
", "rawMarkdown": "I completely agree with your observations about the difficulty of this challenge- pixel-level F1, varying textures, lighting, and even some rectangular GT masks make it really tricky.\n\nOne additional point that is crucial is the extreme asymmetry between forged and authentic pixels: forged pixels are extremely rare, so if the model isn’t trained carefully, it tends to collapse and predict “authentic everywhere,” which limits the score to around 0.30–0.32.\n\nIn my experience, a staged training approach can help solve both problems", "votes": null }, { "id": "3328754", "postDate": "11/16/2025 01:54:34", "content": "hey please help me
", "rawMarkdown": "hey please help me", "votes": null }, { "id": "3328755", "postDate": "11/16/2025 01:58:16", "content": "hey bro how you score has increaase please tell me and help me
", "rawMarkdown": "hey bro how you score has increaase please tell me and help me", "votes": null } ], "comments": [ { "id": 3313178, "author_name": "sararc83", "author_url": "", "post_date": "11/08/2025 20:51:13", "content": "Hi,\nThere is another point: the images haven't the same channels, some of them are in RGB format, another are in RGBA… It´s complicated but it's very funny 😊\nWe have a lot of time and we can enjoy playing with data and models
", "votes": null, "replies": [] }, { "id": 3314098, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/10/2025 04:36:00", "content": "Hey sister how are you my score is not increasing from 0.305 whats the issue please help me
", "votes": null, "replies": [ { "id": 3314129, "author_name": "djamilabenchikh", "author_url": "", "post_date": "11/10/2025 04:59:06", "content": "Hey! I’m stuck like you 😅 \nTry using these parameters:
\nhey sister i use you notebook thanks for that you public your notebook but my score is not increasing
", "votes": null, "replies": [ { "id": 3315183, "author_name": "djamilabenchikh", "author_url": "", "post_date": "11/10/2025 14:05:58", "content": "Hey! I applied the parameters below in the public notebook, and it gave me a small boost (0.307). You can try them or further develop the initial solution.
", "votes": null, "replies": [ { "id": 3318288, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/11/2025 05:08:13", "content": "oh thanks sister
", "votes": null, "replies": [ { "id": 3318289, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/11/2025 05:08:31", "content": "always live happy
", "votes": null, "replies": [] } ] } ] } ] } ] } ] }, { "id": 3318740, "author_name": "isaacmenard", "author_url": "", "post_date": "11/11/2025 10:52:39", "content": "
I also found it hard that some cells are labeled with boxes and not by cell shape. It lost almost half of the F1 points because of that :/
", "votes": null, "replies": [ { "id": 3319027, "author_name": "qifeihhh666", "author_url": "", "post_date": "11/11/2025 14:23:07", "content": "Your observation is keen. Especially when it comes to stripes with unclear boundaries, which are often masked with rectangular shapes, this poses a challenging problem that needs to be addressed.
", "votes": null, "replies": [ { "id": 3328755, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/16/2025 01:58:16", "content": "hey bro how you score has increaase please tell me and help me
", "votes": null, "replies": [] } ] } ] }, { "id": 3324557, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/15/2025 01:50:01", "content": "hey sister i am trying from few days but my score is not increasing what i need to do
", "votes": null, "replies": [] }, { "id": 3325975, "author_name": "nocarbonintelligence", "author_url": "", "post_date": "11/15/2025 08:14:08", "content": "I completely agree with your observations about the difficulty of this challenge- pixel-level F1, varying textures, lighting, and even some rectangular GT masks make it really tricky.
\nOne additional point that is crucial is the extreme asymmetry between forged and authentic pixels: forged pixels are extremely rare, so if the model isn’t trained carefully, it tends to collapse and predict “authentic everywhere,” which limits the score to around 0.30–0.32.
\nIn my experience, a staged training approach can help solve both problems
", "votes": null, "replies": [] }, { "id": 3328754, "author_name": "maheenriaz1122", "author_url": "", "post_date": "11/16/2025 01:54:34", "content": "hey please help me
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3313150": "It’s possible that the test set includes samples with slightly or even noticeably different textures, lighting conditions, or noise characteristics, which can make segmentation more difficult for most models.\n\nThe evaluation metric itself is also extremely demanding, it’s not about detecting the general region, but achieving pixel-level precision.\n\nThe scoring is based on F1 matching between predicted and ground-truth masks, using the Hungarian algorithm to optimally align them:\n\n `def calculate_f1_score(pred_mask, gt_mask):\n tp = np.sum((pred_mask == 1) & (gt_mask == 1))\n fp = np.sum((pred_mask == 1) & (gt_mask == 0))\n fn = np.sum((pred_mask == 0) & (gt_mask == 1))\n precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n return 2 * precision * recall / (precision + recall + 1e-6)\n`\n\nBecause of this, even small spatial misalignments or imperfect mask boundaries can significantly reduce the score, the metric rewards exact overlap rather than approximate localization.\n\nThis explains why even strong architectures such as U-Net, YOLO, or R-CNN often struggle here:\nthey can identify forged regions in general, but not always with the pixel-perfect alignment that the metric expects.\n\nSo the bottleneck is rarely in the inference or RLE code, it lies in the model’s ability to capture fine-grained local boundaries and texture consistency.\nReaching higher scores will likely require models capable of producing sharper, structurally consistent masks.\n\nOne possible direction is introducing contextual or structural refinement mechanisms (e.g. patch-level refinement, edge-aware regularization).\nIt’s not just about applying standard segmentation architectures. \n\n💡it’s about developing a more robust, geometry-aware approach that understands the structure of scientific textures.\n\n💬** If anyone has additional insights or observations about this, feel free to share them here, it would be great to exchange perspectives and deepen our collective understanding of this challenge.**", "3313178": "Hi,\nThere is another point: the images haven't the same channels, some of them are in RGB format, another are in RGBA... It´s complicated but it's very funny 😊\nWe have a lot of time and we can enjoy playing with data and models", "3314098": "Hey sister how are you my score is not increasing from 0.305 whats the issue please help me", "3314129": "Hey! I’m stuck like you 😅 \nTry using these parameters:\n- BATCH_SIZE = 1\n- EPOCHS_SEG = 5\n- LR_SEG = 1e-5\n- WEIGHT_DECAY = 2e-4\nIt gave me a small boost", "3314159": "hey sister i use you notebook thanks for that you public your notebook but my score is not increasing", "3315183": "Hey! I applied the parameters below in the public notebook, and it gave me a small boost (0.307). You can try them or further develop the initial solution.", "3318288": "oh thanks sister", "3318289": "always live happy", "3318740": "\n\nI also found it hard that some cells are labeled with boxes and not by cell shape. It lost almost half of the F1 points because of that :/", "3319027": "Your observation is keen. Especially when it comes to stripes with unclear boundaries, which are often masked with rectangular shapes, this poses a challenging problem that needs to be addressed.", "3324557": "hey sister i am trying from few days but my score is not increasing what i need to do", "3325975": "I completely agree with your observations about the difficulty of this challenge- pixel-level F1, varying textures, lighting, and even some rectangular GT masks make it really tricky.\n\nOne additional point that is crucial is the extreme asymmetry between forged and authentic pixels: forged pixels are extremely rare, so if the model isn’t trained carefully, it tends to collapse and predict “authentic everywhere,” which limits the score to around 0.30–0.32.\n\nIn my experience, a staged training approach can help solve both problems", "3328754": "hey please help me", "3328755": "hey bro how you score has increaase please tell me and help me" }, "source": "meta" }