Reference to recently Completed BirdCLEF + 2025 competition.
\nhttps://www.kaggle.com/competitions/birdclef-2025/
Acknowledgements:
\nThanks for Competition Hosts Cornell Lab of Ornithology and Kaggle for organizing this competition.
\nAlso, congratulations to all the winners.
\nThanks to many of the participants in this competition for active discussions, sharing ideas and sharing notebooks.
I am glad to note the completion of this competition and Bronze medal with 146th rank. I had earlier got
\nsilver medal in BirdCLEF 2024 competition.
Solution details of my approach:
\nThis is a high-performance solution for the BirdCLEF 2025 Kaggle competition, which involves identifying bird species from audio recordings. The notebook implements a sophisticated two-model ensemble strategy to generate a final prediction file. It achieves this by generating two separate submissions and then blending them together. The note book is based on high scoring public notebooks shared by participants in this competition.
Key Highlights
\nTwo-Model Ensemble Strategy: The core of the solution is blending the outputs of two different, powerful models:
\nSubmission 1: Uses an eca_nfnet_l0 model.
\nSubmission 2: Uses a seresnext26t_32x4d model.
\nThis approach leverages the diverse strengths of different architectures to produce a more robust final prediction.
Temporal Smoothing: A crucial post-processing technique is applied to the predictions. It assumes that if a bird is present in one 5-second audio chunk, it is likely to be present in the adjacent chunks. The code enforces this by smoothing the predictions using a weighted average of a chunk and its immediate neighbors (e.g., new_pred = 0.2previous + 0.6current + 0.2*next).
\nPower Adjustment for Low-Confidence Predictions: The code uses a clever function to boost the scores of predictions that are not in the top-k most confident classes. This helps to amplify weak signals and improve the overall evaluation metric.
\nFinal Weighted Blending: After generating two complete submission files, the notebook combines them using a final weighted average. The final weights used are [0.85, 0.3], giving more importance to the SEResNeXt model's output.
\nRounding to 4 decimal Points: We round of the final outputs to 4 decimal places. I am quoting here a practical strategy which gives good results by removing unnecessary noisy digits.
\n\"Rounding to the nearest cent is sufficiently accurate for practical purposes.\"- Alexander John Ellis
In summary, solution relies on ensembling strong models and applying domain-specific post-processing (temporal smoothing) to achieve good results.
\nReferences:
\nThe models can be extended for Endangered species of wild animals.
\nUsage of GPUs
Reference to recently Completed BirdCLEF + 2025 competition.
\nhttps://www.kaggle.com/competitions/birdclef-2025/
Acknowledgements:
\nThanks for Competition Hosts Cornell Lab of Ornithology and Kaggle for organizing this competition.
\nAlso, congratulations to all the winners.
\nThanks to many of the participants in this competition for active discussions, sharing ideas and sharing notebooks.
I am glad to note the completion of this competition and Bronze medal with 146th rank. I had earlier got
\nsilver medal in BirdCLEF 2024 competition.
Solution details of my approach:
\nThis is a high-performance solution for the BirdCLEF 2025 Kaggle competition, which involves identifying bird species from audio recordings. The notebook implements a sophisticated two-model ensemble strategy to generate a final prediction file. It achieves this by generating two separate submissions and then blending them together. The note book is based on high scoring public notebooks shared by participants in this competition.
Key Highlights
\nTwo-Model Ensemble Strategy: The core of the solution is blending the outputs of two different, powerful models:
\nSubmission 1: Uses an eca_nfnet_l0 model.
\nSubmission 2: Uses a seresnext26t_32x4d model.
\nThis approach leverages the diverse strengths of different architectures to produce a more robust final prediction.
Temporal Smoothing: A crucial post-processing technique is applied to the predictions. It assumes that if a bird is present in one 5-second audio chunk, it is likely to be present in the adjacent chunks. The code enforces this by smoothing the predictions using a weighted average of a chunk and its immediate neighbors (e.g., new_pred = 0.2previous + 0.6current + 0.2*next).
\nPower Adjustment for Low-Confidence Predictions: The code uses a clever function to boost the scores of predictions that are not in the top-k most confident classes. This helps to amplify weak signals and improve the overall evaluation metric.
\nFinal Weighted Blending: After generating two complete submission files, the notebook combines them using a final weighted average. The final weights used are [0.85, 0.3], giving more importance to the SEResNeXt model's output.
\nRounding to 4 decimal Points: We round of the final outputs to 4 decimal places. I am quoting here a practical strategy which gives good results by removing unnecessary noisy digits.
\n\"Rounding to the nearest cent is sufficiently accurate for practical purposes.\"- Alexander John Ellis
In summary, solution relies on ensembling strong models and applying domain-specific post-processing (temporal smoothing) to achieve good results.
\nReferences:
\nThe models can be extended for Endangered species of wild animals.
\nUsage of GPUs