{ "id": 583437, "title": "What to Take into BirdCLEF 2026", "url": "/competitions/birdclef-2025/discussion/583437", "author_name": "", "post_date": "2025-06-06T20:24:14.786683400Z", "votes": null, "comment_count": 2, "views": 0, "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
✅ What to Do💡 Why it Matters
Build robust SED + attention block architectureOutperforms CNNs in localization and robustness
Start early with voice-removal toolingSaves you weeks of leaderboard confusion
Expect to run 5+ rounds of self-distillationIt’s the new normal—not just a trick
Design for speed: ONNX/OpenVINO + overlap windowing600s+ total inference = a timeout trap
Track class distribution & inject class-aware postprocessingMacro-AUC punishes rare-class neglect
❌ Don’t overinvest in exotic backbones without ensemble diversitySED + strong fundamentals ≫ fancy solo models
\n

A detailed summary on winners solutions available here: https://www.kaggle.com/competitions/birdclef-2025/discussion/583434

", "messages": [ { "id": "3218866", "postDate": "06/06/2025 20:24:14", "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
✅ What to Do💡 Why it Matters
Build robust SED + attention block architectureOutperforms CNNs in localization and robustness
Start early with voice-removal toolingSaves you weeks of leaderboard confusion
Expect to run 5+ rounds of self-distillationIt’s the new normal—not just a trick
Design for speed: ONNX/OpenVINO + overlap windowing600s+ total inference = a timeout trap
Track class distribution & inject class-aware postprocessingMacro-AUC punishes rare-class neglect
❌ Don’t overinvest in exotic backbones without ensemble diversitySED + strong fundamentals ≫ fancy solo models
\n

A detailed summary on winners solutions available here: https://www.kaggle.com/competitions/birdclef-2025/discussion/583434

", "rawMarkdown": "| ✅ What to Do | 💡 Why it Matters |\n|--------------|-------------------|\n| **Build robust SED + attention block architecture** | Outperforms CNNs in localization and robustness |\n| **Start early with voice-removal tooling** | Saves you weeks of leaderboard confusion |\n| **Expect to run 5+ rounds of self-distillation** | It’s the new normal—not just a trick |\n| **Design for speed: ONNX/OpenVINO + overlap windowing** | 600s+ total inference = a timeout trap |\n| **Track class distribution & inject class-aware postprocessing** | Macro-AUC punishes rare-class neglect |\n| ❌ **Don’t overinvest in exotic backbones without ensemble diversity** | SED + strong fundamentals ≫ fancy solo models |\nA detailed summary on winners solutions available here: https://www.kaggle.com/competitions/birdclef-2025/discussion/583434\n------", "votes": null }, { "id": "3218914", "postDate": "06/06/2025 23:17:16", "content": "

SED may not always outperform CNN though.

", "rawMarkdown": "SED may not always outperform CNN though.", "votes": null }, { "id": "3219768", "postDate": "06/08/2025 09:40:15", "content": "

I assume particularly in problems like this, SED performs better. CNN is still a more general and relevant approach for most image related problems.
\nAgain, this is the first time I learnt about SED and still understanding it properly.

", "rawMarkdown": "I assume particularly in problems like this, SED performs better. CNN is still a more general and relevant approach for most image related problems. \nAgain, this is the first time I learnt about SED and still understanding it properly.", "votes": null } ], "comments": [ { "id": 3218914, "author_name": "shanzhong8", "author_url": "", "post_date": "06/06/2025 23:17:16", "content": "

SED may not always outperform CNN though.

", "votes": null, "replies": [ { "id": 3219768, "author_name": "aayush26", "author_url": "", "post_date": "06/08/2025 09:40:15", "content": "

I assume particularly in problems like this, SED performs better. CNN is still a more general and relevant approach for most image related problems.
\nAgain, this is the first time I learnt about SED and still understanding it properly.

", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "3218866": "| ✅ What to Do | 💡 Why it Matters |\n|--------------|-------------------|\n| **Build robust SED + attention block architecture** | Outperforms CNNs in localization and robustness |\n| **Start early with voice-removal tooling** | Saves you weeks of leaderboard confusion |\n| **Expect to run 5+ rounds of self-distillation** | It’s the new normal—not just a trick |\n| **Design for speed: ONNX/OpenVINO + overlap windowing** | 600s+ total inference = a timeout trap |\n| **Track class distribution & inject class-aware postprocessing** | Macro-AUC punishes rare-class neglect |\n| ❌ **Don’t overinvest in exotic backbones without ensemble diversity** | SED + strong fundamentals ≫ fancy solo models |\nA detailed summary on winners solutions available here: https://www.kaggle.com/competitions/birdclef-2025/discussion/583434\n------", "3218914": "SED may not always outperform CNN though.", "3219768": "I assume particularly in problems like this, SED performs better. CNN is still a more general and relevant approach for most image related problems. \nAgain, this is the first time I learnt about SED and still understanding it properly." }, "source": "meta" }