nohup: ignoring input
[INFO] Task1 root: /home/huangyingqian/code/CV/task1
[INFO] Python: /home/huangyingqian/miniconda3/envs/m/bin/python
[INFO] torch=2.6.0+cu124 cuda_available=True cuda_devices=7
[INFO] timm=1.0.26
[INFO] Preparing Oxford-IIIT Pet dataset once before parallel jobs...

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[INFO] dataset ready: {'train': 3129, 'val': 551, 'test': 3669}
====================
[STEP 1] Model comparison
====================
[RUN] gpu=0 name=resnet18_pretrained cmd=python train.py --config configs/resnet18_pretrained.yaml
[RUN] gpu=1 name=resnet18_scratch cmd=python train.py --config configs/resnet18_scratch.yaml
[RUN] gpu=2 name=se_resnet18_pretrained cmd=python train.py --config configs/se_resnet18_pretrained.yaml
[RUN] gpu=3 name=cbam_resnet18_pretrained cmd=python train.py --config configs/cbam_resnet18_pretrained.yaml
[RUN] gpu=4 name=vit_tiny_pretrained cmd=python train.py --config configs/vit_tiny_pretrained.yaml
[RUN] gpu=5 name=swin_t_pretrained cmd=python train.py --config configs/swin_t_pretrained.yaml
[OK] resnet18_pretrained
[OK] resnet18_scratch
[OK] se_resnet18_pretrained
[OK] cbam_resnet18_pretrained
[OK] vit_tiny_pretrained
[OK] swin_t_pretrained
[OK] Wrote 6 rows to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_summary.csv
========================================================================
Top 10 by best_val_acc
========================================================================
01. swin_t_pretrained                    best_val_acc=0.9637  test_acc=0.9250  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_swin_t_pretrained
02. resnet18_pretrained                  best_val_acc=0.9401  test_acc=0.8793  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_resnet18_pretrained
03. vit_tiny_pretrained                  best_val_acc=0.9401  test_acc=0.8986  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_vit_tiny_pretrained
04. se_resnet18_pretrained               best_val_acc=0.9292  test_acc=0.8910  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_se_resnet18_pretrained
05. cbam_resnet18_pretrained             best_val_acc=0.9183  test_acc=0.8678  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_cbam_resnet18_pretrained
06. resnet18_scratch                     best_val_acc=0.5953  test_acc=0.5110  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_resnet18_scratch
[OK] Plots saved to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_plots
====================
[STEP 2] Hyperparameter phase 1: learning rate search
====================
[RUN] gpu=0 name=hparam_p1_lr_3e-5_3e-4
[RUN] gpu=1 name=hparam_p1_lr_5e-5_5e-4
[RUN] gpu=2 name=hparam_p1_lr_1e-4_1e-3
[RUN] gpu=3 name=hparam_p1_lr_2e-4_2e-3
[RUN] gpu=4 name=hparam_p1_lr_3e-4_3e-3
[RUN] gpu=5 name=hparam_p1_lr_1e-4_5e-4
[RUN] gpu=6 name=hparam_p1_lr_1e-4_2e-3
[OK] hparam_p1_lr_3e-5_3e-4
[OK] hparam_p1_lr_5e-5_5e-4
[OK] hparam_p1_lr_1e-4_1e-3
[OK] hparam_p1_lr_2e-4_2e-3
[OK] hparam_p1_lr_3e-4_3e-3
[OK] hparam_p1_lr_1e-4_5e-4
[OK] hparam_p1_lr_1e-4_2e-3
[OK] Phase 1 done. Logs are under logs/hparam/.
[OK] Wrote 13 rows to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_summary.csv
========================================================================
Top 10 by best_val_acc
========================================================================
01. swin_t_pretrained                    best_val_acc=0.9637  test_acc=0.9250  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_swin_t_pretrained
02. hparam_p1_lr_3e-5_3e-4               best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4
03. hparam_p1_lr_1e-4_5e-4               best_val_acc=0.9437  test_acc=0.8872  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4
04. hparam_p1_lr_1e-4_2e-3               best_val_acc=0.9419  test_acc=0.8844  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3
05. hparam_p1_lr_5e-5_5e-4               best_val_acc=0.9419  test_acc=0.8962  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4
06. resnet18_pretrained                  best_val_acc=0.9401  test_acc=0.8793  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_resnet18_pretrained
07. vit_tiny_pretrained                  best_val_acc=0.9401  test_acc=0.8986  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_vit_tiny_pretrained
08. hparam_p1_lr_1e-4_1e-3               best_val_acc=0.9401  test_acc=0.8793  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3
09. hparam_p1_lr_2e-4_2e-3               best_val_acc=0.9365  test_acc=0.8719  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3
10. se_resnet18_pretrained               best_val_acc=0.9292  test_acc=0.8910  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_se_resnet18_pretrained
[OK] Plots saved to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_plots
[INFO] best_phase1=hparam_p1_lr_3e-5_3e-4 best_val_acc=0.947368
[INFO] Using BEST_BACKBONE_LR=3e-05 BEST_HEAD_LR=0.0003
====================
[STEP 3] Hyperparameter phase 2: epoch and regularization
====================
[RUN] gpu=0 name=hparam_p2_bestlr_ep10
[RUN] gpu=1 name=hparam_p2_bestlr_ep20
[RUN] gpu=2 name=hparam_p2_bestlr_ep30
[RUN] gpu=3 name=hparam_p2_bestlr_wd_5e-5
[RUN] gpu=4 name=hparam_p2_bestlr_wd_5e-4
[RUN] gpu=5 name=hparam_p2_bestlr_dropout_0p2
[RUN] gpu=6 name=hparam_p2_bestlr_ls_0p1
[OK] hparam_p2_bestlr_ep10
[OK] hparam_p2_bestlr_ep20
[OK] hparam_p2_bestlr_ep30
[OK] hparam_p2_bestlr_wd_5e-5
[OK] hparam_p2_bestlr_wd_5e-4
[OK] hparam_p2_bestlr_dropout_0p2
[OK] hparam_p2_bestlr_ls_0p1
[OK] Phase 2 done. Logs are under logs/hparam/.
[OK] Wrote 20 rows to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_summary.csv
========================================================================
Top 10 by best_val_acc
========================================================================
01. swin_t_pretrained                    best_val_acc=0.9637  test_acc=0.9250  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_swin_t_pretrained
02. hparam_p2_bestlr_dropout_0p2         best_val_acc=0.9492  test_acc=0.8945  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2
03. hparam_p2_bestlr_wd_5e-4             best_val_acc=0.9492  test_acc=0.8940  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4
04. hparam_p1_lr_3e-5_3e-4               best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4
05. hparam_p2_bestlr_ep20                best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_ep20
06. hparam_p1_lr_1e-4_5e-4               best_val_acc=0.9437  test_acc=0.8872  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4
07. hparam_p2_bestlr_ep30                best_val_acc=0.9437  test_acc=0.8962  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_ep30
08. hparam_p2_bestlr_ls_0p1              best_val_acc=0.9437  test_acc=0.8959  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_ls_0p1
09. hparam_p2_bestlr_wd_5e-5             best_val_acc=0.9437  test_acc=0.8948  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5
10. hparam_p1_lr_1e-4_2e-3               best_val_acc=0.9419  test_acc=0.8844  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3
[OK] Plots saved to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_plots
[INFO] best_phase2_epoch=hparam_p2_bestlr_ep20 best_val_acc=0.947368
[INFO] best_phase2_wd=hparam_p2_bestlr_wd_5e-4 best_val_acc=0.949183
[INFO] Using BEST_EPOCHS=20 BEST_WEIGHT_DECAY=0.0005
====================
[STEP 4] Hyperparameter phase 3: final combinations
====================
[RUN] gpu=0 name=hparam_p3_final_lr_ep
[RUN] gpu=1 name=hparam_p3_final_lr_wd
[RUN] gpu=2 name=hparam_p3_final_lr_dropout
[RUN] gpu=3 name=hparam_p3_final_lr_ls
[RUN] gpu=4 name=hparam_p3_final_lr_ep_wd
[RUN] gpu=5 name=hparam_p3_final_lr_ep_wd_dropout
[RUN] gpu=6 name=hparam_p3_final_lr_ep_wd_ls
[OK] hparam_p3_final_lr_ep
[OK] hparam_p3_final_lr_wd
[OK] hparam_p3_final_lr_dropout
[OK] hparam_p3_final_lr_ls
[OK] hparam_p3_final_lr_ep_wd
[OK] hparam_p3_final_lr_ep_wd_dropout
[OK] hparam_p3_final_lr_ep_wd_ls
[OK] Phase 3 done. Logs are under logs/hparam/.
[OK] Wrote 27 rows to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_summary.csv
========================================================================
Top 10 by best_val_acc
========================================================================
01. swin_t_pretrained                    best_val_acc=0.9637  test_acc=0.9250  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030126_swin_t_pretrained
02. hparam_p2_bestlr_dropout_0p2         best_val_acc=0.9492  test_acc=0.8945  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2
03. hparam_p2_bestlr_wd_5e-4             best_val_acc=0.9492  test_acc=0.8940  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4
04. hparam_p3_final_lr_dropout           best_val_acc=0.9492  test_acc=0.8945  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031627_hparam_p3_final_lr_dropout
05. hparam_p3_final_lr_ep_wd             best_val_acc=0.9492  test_acc=0.8940  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031627_hparam_p3_final_lr_ep_wd
06. hparam_p3_final_lr_ep_wd_dropout     best_val_acc=0.9492  test_acc=0.8953  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout
07. hparam_p3_final_lr_wd                best_val_acc=0.9492  test_acc=0.8940  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031627_hparam_p3_final_lr_wd
08. hparam_p1_lr_3e-5_3e-4               best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4
09. hparam_p2_bestlr_ep20                best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031052_hparam_p2_bestlr_ep20
10. hparam_p3_final_lr_ep                best_val_acc=0.9474  test_acc=0.8951  dir=/mnt/data/huangyingqian/code/CV/task1/outputs/20260429_031627_hparam_p3_final_lr_ep
[OK] Plots saved to /mnt/data/huangyingqian/code/CV/task1/outputs/hparam_plots
====================
[DONE] All Task1 experiments finished
====================
outputs/20260429_030126_cbam_resnet18_pretrained/accuracy_curve.png
outputs/20260429_030126_cbam_resnet18_pretrained/loss_curve.png
outputs/20260429_030126_cbam_resnet18_pretrained/metrics.csv
outputs/20260429_030126_cbam_resnet18_pretrained/summary.json
outputs/20260429_030126_cbam_resnet18_pretrained/training_curves.png
outputs/20260429_030126_resnet18_pretrained/accuracy_curve.png
outputs/20260429_030126_resnet18_pretrained/loss_curve.png
outputs/20260429_030126_resnet18_pretrained/metrics.csv
outputs/20260429_030126_resnet18_pretrained/summary.json
outputs/20260429_030126_resnet18_pretrained/training_curves.png
outputs/20260429_030126_resnet18_scratch/accuracy_curve.png
outputs/20260429_030126_resnet18_scratch/loss_curve.png
outputs/20260429_030126_resnet18_scratch/metrics.csv
outputs/20260429_030126_resnet18_scratch/summary.json
outputs/20260429_030126_resnet18_scratch/training_curves.png
outputs/20260429_030126_se_resnet18_pretrained/accuracy_curve.png
outputs/20260429_030126_se_resnet18_pretrained/loss_curve.png
outputs/20260429_030126_se_resnet18_pretrained/metrics.csv
outputs/20260429_030126_se_resnet18_pretrained/summary.json
outputs/20260429_030126_se_resnet18_pretrained/training_curves.png
outputs/20260429_030126_swin_t_pretrained/accuracy_curve.png
outputs/20260429_030126_swin_t_pretrained/loss_curve.png
outputs/20260429_030126_swin_t_pretrained/metrics.csv
outputs/20260429_030126_swin_t_pretrained/summary.json
outputs/20260429_030126_swin_t_pretrained/training_curves.png
outputs/20260429_030126_vit_tiny_pretrained/accuracy_curve.png
outputs/20260429_030126_vit_tiny_pretrained/loss_curve.png
outputs/20260429_030126_vit_tiny_pretrained/metrics.csv
outputs/20260429_030126_vit_tiny_pretrained/summary.json
outputs/20260429_030126_vit_tiny_pretrained/training_curves.png
outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3/metrics.csv
outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3/summary.json
outputs/20260429_030657_hparam_p1_lr_1e-4_1e-3/training_curves.png
outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3/metrics.csv
outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3/summary.json
outputs/20260429_030657_hparam_p1_lr_1e-4_2e-3/training_curves.png
outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4/metrics.csv
outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4/summary.json
outputs/20260429_030657_hparam_p1_lr_1e-4_5e-4/training_curves.png
outputs/20260429_030657_hparam_p1_lr_3e-4_3e-3/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_3e-4_3e-3/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_3e-4_3e-3/metrics.csv
outputs/20260429_030657_hparam_p1_lr_3e-4_3e-3/summary.json
outputs/20260429_030657_hparam_p1_lr_3e-4_3e-3/training_curves.png
outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4/metrics.csv
outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4/summary.json
outputs/20260429_030657_hparam_p1_lr_3e-5_3e-4/training_curves.png
outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4/accuracy_curve.png
outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4/loss_curve.png
outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4/metrics.csv
outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4/summary.json
outputs/20260429_030657_hparam_p1_lr_5e-5_5e-4/training_curves.png
outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3/accuracy_curve.png
outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3/loss_curve.png
outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3/metrics.csv
outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3/summary.json
outputs/20260429_030658_hparam_p1_lr_2e-4_2e-3/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2/summary.json
outputs/20260429_031052_hparam_p2_bestlr_dropout_0p2/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_ep10/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep10/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep10/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_ep10/summary.json
outputs/20260429_031052_hparam_p2_bestlr_ep10/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_ep20/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep20/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep20/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_ep20/summary.json
outputs/20260429_031052_hparam_p2_bestlr_ep20/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_ep30/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep30/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ep30/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_ep30/summary.json
outputs/20260429_031052_hparam_p2_bestlr_ep30/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_ls_0p1/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ls_0p1/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_ls_0p1/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_ls_0p1/summary.json
outputs/20260429_031052_hparam_p2_bestlr_ls_0p1/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4/summary.json
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-4/training_curves.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5/accuracy_curve.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5/loss_curve.png
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5/metrics.csv
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5/summary.json
outputs/20260429_031052_hparam_p2_bestlr_wd_5e-5/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_dropout/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_dropout/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_dropout/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_dropout/summary.json
outputs/20260429_031627_hparam_p3_final_lr_dropout/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_ep/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_ep/summary.json
outputs/20260429_031627_hparam_p3_final_lr_ep/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout/summary.json
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_dropout/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_ls/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_ls/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_ls/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_ls/summary.json
outputs/20260429_031627_hparam_p3_final_lr_ep_wd_ls/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_ep_wd/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_ep_wd/summary.json
outputs/20260429_031627_hparam_p3_final_lr_ep_wd/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_ls/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ls/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_ls/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_ls/summary.json
outputs/20260429_031627_hparam_p3_final_lr_ls/training_curves.png
outputs/20260429_031627_hparam_p3_final_lr_wd/accuracy_curve.png
outputs/20260429_031627_hparam_p3_final_lr_wd/loss_curve.png
outputs/20260429_031627_hparam_p3_final_lr_wd/metrics.csv
outputs/20260429_031627_hparam_p3_final_lr_wd/summary.json
outputs/20260429_031627_hparam_p3_final_lr_wd/training_curves.png
outputs/hparam_plots/phase1_lr_best_val_acc.png
outputs/hparam_plots/phase2_reg_epoch_best_val_acc.png
outputs/hparam_plots/top10_best_val_acc.png
outputs/hparam_summary.csv