The objective of this challenge is to classify agricultural audio keywords in English and Lugandan from Uganda. For more information about this challenge, have a look on Zindi.

Our solution consist of a mix of Resnet34 and Resnet50. During the training, we used a single augmentation (SpecAugmentation) strategy from torchlibrosa, which randomly cutout/erase some parts of the spectogram along the frequency and time dimensions.

• Resnet34 was the best scorer with a public leaderboard of 0.84 (Logloss).
• Resnet50 followed with a public LB of 0.88.
• A simple average of 2 Resnet34 gave us 0.8075 on the public LB.
• Our final solution was a weighted average og the Resnet50 and 2 x Resnet34, for a small boost of -0.06.

NB: We tried other architectures like Densenet, Efficienet, but it seemed that they were overfitting (LB of 1.+)

You must follow this structure when runing the notebooks.

|----data
|       |--- raw
|              |--- audio_files.zip
|              |--- AdditionalUtterances.zip
|              |--- nlp_keywords_29Oct2020.zip
|              |--- train.csv
|              |--- SampleSubmission.csv
|
|---- Resnet34_base.ipynb
|---- Resnet34_with_scheduler.ipynb
|---- Resnet50_base.ipynb
|---- blend.ipynb
|---- Readme.md

Here is how to run the code:

# 1- Run {Resnet34_base, Resnet34_with_scheduler, Resnet50_base}.ipynb

# 2- Run blend.ipynb to generate the final submission file

🏅Position: 7th
🏅Score: 0.8019 (LogLoss)

See the leaderboard

With 💖 from the Wakanda Team.

MIT License

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Copyright (c) 2020 TheWakandaTeam.
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## Final Leaderboard
>>>>>>> 23c9dbce4e880024d23d8a776913ea4b51727263

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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