![\"solution\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F27295865%2F569641f3e075a126bbf58d412f12f53a%2Fsolution.jpg?generation=1751361162365794&alt=media\")
This competition was an excellent experience! Not only was there a strong correlation between the local cross-validation (CV) scores and the public leaderboard, but the public and private leaderboard scores were also nearly identical. This is likely due to the large number of samples in the test set—according to statistical theory, such stability is reasonable.
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Here are some key experiences and points to note from our team’s solution, as illustrated below:
\n![\"GPU\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F27295865%2Fab0f50fbfcf35dcc3c93fd217cb014f9%2FGPU.jpg?generation=1751361175081967&alt=media\")
batch_size = 16 is particularly important. Even if your GPU has sufficient memory, it’s not recommended to set the batch size too large, as it can slow down convergence. Conversely, setting it too small may also result in slower convergence. In the later stages of training, it is advisable to increase the patience parameter slightly to avoid premature termination.For more details about our code or strategies, feel free to reach out!
", "messages": [ { "id": "3237730", "postDate": "07/01/2025 09:13:33", "content": "This competition was an excellent experience! Not only was there a strong correlation between the local cross-validation (CV) scores and the public leaderboard, but the public and private leaderboard scores were also nearly identical. This is likely due to the large number of samples in the test set—according to statistical theory, such stability is reasonable.
\n
Here are some key experiences and points to note from our team’s solution, as illustrated below:
\n
batch_size = 16 is particularly important. Even if your GPU has sufficient memory, it’s not recommended to set the batch size too large, as it can slow down convergence. Conversely, setting it too small may also result in slower convergence. In the later stages of training, it is advisable to increase the patience parameter slightly to avoid premature termination.For more details about our code or strategies, feel free to reach out!
", "rawMarkdown": "This competition was an excellent experience! Not only was there a strong correlation between the local cross-validation (CV) scores and the public leaderboard, but the public and private leaderboard scores were also nearly identical. This is likely due to the large number of samples in the test set—according to statistical theory, such stability is reasonable.\n\n## Our Team's Solution Workflow\n\n\n\n---\n\n## Key Takeaways and Notes\n\nHere are some key experiences and points to note from our team’s solution, as illustrated below:\n\n\n\n---\n\n### Highlights\n\n- **Training Parameter Settings** \n - Setting `batch_size = 16` is particularly important. Even if your GPU has sufficient memory, it’s not recommended to set the batch size too large, as it can slow down convergence. Conversely, setting it too small may also result in slower convergence. In the later stages of training, it is advisable to increase the patience parameter slightly to avoid premature termination.\n- **Hardware Utilization** \n - Make full use of various GPU devices (e.g., H800, 5090, 4090) and allocate resources efficiently to improve experimentation efficiency.\n- **Model Ensembling** \n - Use multiple backbones, stacking, and median ensembling methods to improve model robustness and final results.\n\n---\n\nFor more details about our code or strategies, feel free to reach out!", "votes": null }, { "id": "3239114", "postDate": "07/02/2025 13:52:53", "content": "Hello, congratulations! For the CaFormer branch, you used two different backbones—how did you end up with four model weights as output?
", "rawMarkdown": "Hello, congratulations! For the CaFormer branch, you used two different backbones—how did you end up with four model weights as output?", "votes": null } ], "comments": [ { "id": 3239114, "author_name": "justforfun44", "author_url": "", "post_date": "07/02/2025 13:52:53", "content": "Hello, congratulations! For the CaFormer branch, you used two different backbones—how did you end up with four model weights as output?
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3237730": "This competition was an excellent experience! Not only was there a strong correlation between the local cross-validation (CV) scores and the public leaderboard, but the public and private leaderboard scores were also nearly identical. This is likely due to the large number of samples in the test set—according to statistical theory, such stability is reasonable.\n\n## Our Team's Solution Workflow\n\n\n\n---\n\n## Key Takeaways and Notes\n\nHere are some key experiences and points to note from our team’s solution, as illustrated below:\n\n\n\n---\n\n### Highlights\n\n- **Training Parameter Settings** \n - Setting `batch_size = 16` is particularly important. Even if your GPU has sufficient memory, it’s not recommended to set the batch size too large, as it can slow down convergence. Conversely, setting it too small may also result in slower convergence. In the later stages of training, it is advisable to increase the patience parameter slightly to avoid premature termination.\n- **Hardware Utilization** \n - Make full use of various GPU devices (e.g., H800, 5090, 4090) and allocate resources efficiently to improve experimentation efficiency.\n- **Model Ensembling** \n - Use multiple backbones, stacking, and median ensembling methods to improve model robustness and final results.\n\n---\n\nFor more details about our code or strategies, feel free to reach out!", "3239114": "Hello, congratulations! For the CaFormer branch, you used two different backbones—how did you end up with four model weights as output?" }, "source": "meta" }