A general deep learning pipeline (in construction) for kaggle competitions and other projects.
Setup with pip:
pip install dl_pipeline
Clone and editable setup:
git clone https://github.com/mnpinto/dl_pipeline
cd dl_pipeline
pip install -e .-
Final ranking: 29th place (top 3%)
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Final score: 0.940
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Best single model (5-fold): 0.931
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Train time for 5-folds of best single model (gtx 1080, i7-7700): ~150 min
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Writeup: https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/220306
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Update 1: With this post processing the final score improves to 0.950 and R
#!/bin/bash
arch='densenet121'
model_name='model_0'
sample_rate=32000
n_mels=128
hop_length=640
for fold in 0 1 2 3 4
do
echo "Training $model for fold $fold"
kaggle_rainforest2021 --fold $fold --model_name $model_name \
--model_arch $arch --sample_rate $sample_rate --n_mels $n_mels \
--hop_length $hop_length --bs 32 --head_ps 0.8 \
--tile_width 1024 --mixup true >> log.train
done
for tw in 64 128 256
do
echo "Generate predictions for $model with tile_width of $tw"
kaggle_rainforest2021 --run_test true --model_name $model_name \
--model_arch $arch --sample_rate $sample_rate --n_mels $n_mels \
--hop_length $hop_length --tile_width $tw \
--save_preds true >> log.predict
done