| # ============================================================================ | |
| # 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 | |