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820fab0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | #!/bin/bash
# ============================================================================
# run_paper_experiments.sh (MINIMAL — reuse existing genuine models)
#
# All deep models (U-Net, Attention U-Net, SegFormer, Swin-UNet, Swin ensemble)
# are ALREADY trained and tested on the held-out test set — those results are
# genuine and are reused as-is (no retraining).
#
# The ONLY genuinely missing experiment is the classical Random-Forest baseline,
# which this script runs (CPU, fast). It then prints the complete results table.
# ============================================================================
source /data1/nasiruddink/miniconda3/etc/profile.d/conda.sh
conda activate brats
export TF_CPP_MIN_LOG_LEVEL=2
cd /data1/nasiruddink/rockglacier/RockGlacierNet
echo "############################################################"
echo "# Random-Forest baseline (classical ML on the 20 bands) "
echo "############################################################"
if [ -f bandselection/rf_baseline/metrics.json ]; then
echo "[skip] RF baseline already done"
else
echo "[RF $(date +%H:%M)] training random forest (CPU, ~5-15 min)..."
python3 rf_baseline.py 2>&1 | tail -20
fi
echo ""
echo "############################################################"
echo "# Collecting all results "
echo "############################################################"
python3 collect_paper_results.py
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