| /opt/venv/lib/python3.10/site-packages/apex/transformer/functional/fused_rope.py:49: UserWarning: Aiter backend is selected for fused RoPE. This has lower precision. To disable aiter, export USE_ROCM_AITER_ROPE_BACKEND=0 |
| warnings.warn("Aiter backend is selected for fused RoPE. This has lower precision. To disable aiter, export USE_ROCM_AITER_ROPE_BACKEND=0", UserWarning) |
| /opt/venv/lib/python3.10/site-packages/timm/models/layers/__init__.py:49: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers |
| warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) |
| /opt/venv/lib/python3.10/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4317.) |
| return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] |
|
|
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
| [1m[96m AgriFM × PASTIS — Fold 5 | 2026-04-17 10:08:55[0m |
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
|
|
| Device : |
| VRAM : 191.7 GB |
| Model size : small (~14M) |
| AMP : True |
| Batch size : 16 |
| Epochs : 100 |
| LR : 5e-05 |
| Weight decay : 0.05 |
| Warmup iters : 500 |
| Augmentation : True (flip + rotate) |
| Class weights : True |
| Work dir : ./work_dirs/fold5_small |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Building Datasets[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| PASTIS fold=5 split=train: 1450 patches (augment=True) |
| PASTIS fold=5 split=val: 487 patches (augment=False) |
| PASTIS fold=5 split=test: 496 patches (augment=False) |
|
|
| Train: 1450 Val: 487 Test: 496 |
| Train batches: 90 Val batches: 31 |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Building Model[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Total params : 39.6M |
| Trainable params : 39.6M |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Computing Class Weights[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Class weights (fold=5, sampled 300 patches): |
| Class Count Weight |
| ────────────────────────────── ────────── ──────── |
| Background 1642825 0.014 |
| Meadow 1138636 0.020 |
| Soft winter wheat 424417 0.054 |
| Corn 621754 0.037 |
| Winter barley 127908 0.178 |
| Winter rapeseed 106032 0.214 |
| Spring barley 37554 0.605 |
| Sunflower 22312 1.018 |
| Grapevine 33841 0.671 |
| Beet 44520 0.510 |
| Winter triticale 52622 0.432 |
| Winter durum wheat 3598 6.313 |
| Fruits vegetables flowers 23068 0.985 |
| Potatoes 16585 1.370 |
| Leguminous fodder 60374 0.376 |
| Soybeans 83584 0.272 |
| Orchard 7435 3.055 |
| Mixed cereal 30483 0.745 |
| Sorghum 10652 2.132 |
|
|
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
| [1m[96m Training | 100 epochs | model=small (~14M) | fold=5[0m |
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
|
|
| [1mTraining[0m |
| MIOpen(HIP): Error [Init] Not found :31-DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<256, 128, 128, 64, Default, 32, 32, 2, 2, 8, 8, 8, 1, 1, BlkGemmPipelineScheduler: Intrawave, BlkGemmPipelineVersion: v3> |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=1.0169 lr=5.90e-06 eta=0:00:10 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.9093 lr=9.82e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.9093 time=0:00:19[0m |
|
|
| [1mValidating...[0m |
| MIOpen(HIP): Error [Init] Not found :31-DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<256, 128, 128, 64, Default, 32, 32, 2, 2, 8, 8, 8, 1, 1, BlkGemmPipelineScheduler: Intrawave, BlkGemmPipelineVersion: v3> |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 001 / 100 | Elapsed: 0:00:27[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.9093 |
| Val Loss 0.7666 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 3.84%[0m |
| [92m[1mmIoU [0m [92m[1m 2.58%[0m |
| [92m[1mmFscore [0m [92m[1m 4.87%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 7.41%[0m |
| [0m[1mmRecall [0m [0m[1m 14.58%[0m |
| [0m[1mKappa [0m [0m[1m 2.28%[0m |
| ────────────────── ────────── |
| Best mFscore 0.00% |
| Val Time 6.6s |
|
|
| [92m[1m★ New best mFscore: 4.87% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.6840 lr=1.47e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.6372 lr=1.86e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.6372 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 002 / 100 | Elapsed: 0:00:46[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.6372 |
| Val Loss 0.5683 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 8.66%[0m |
| [92m[1mmIoU [0m [92m[1m 5.32%[0m |
| [92m[1mmFscore [0m [92m[1m 9.73%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 9.50%[0m |
| [0m[1mmRecall [0m [0m[1m 29.09%[0m |
| [0m[1mKappa [0m [0m[1m 7.18%[0m |
| ────────────────── ────────── |
| Best mFscore 4.87% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 9.73% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.5564 lr=2.35e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.5324 lr=2.75e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.5324 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 003 / 100 | Elapsed: 0:01:05[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.5324 |
| Val Loss 0.5168 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 9.49%[0m |
| [92m[1mmIoU [0m [92m[1m 6.67%[0m |
| [92m[1mmFscore [0m [92m[1m 11.82%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 12.89%[0m |
| [0m[1mmRecall [0m [0m[1m 34.85%[0m |
| [0m[1mKappa [0m [0m[1m 8.07%[0m |
| ────────────────── ────────── |
| Best mFscore 9.73% |
| Val Time 3.8s |
|
|
| [92m[1m★ New best mFscore: 11.82% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4595 lr=3.24e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.4567 lr=3.63e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.4567 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 004 / 100 | Elapsed: 0:01:25[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.4567 |
| Val Loss 0.4640 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 11.27%[0m |
| [92m[1mmIoU [0m [92m[1m 9.70%[0m |
| [92m[1mmFscore [0m [92m[1m 16.37%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 18.30%[0m |
| [0m[1mmRecall [0m [0m[1m 41.11%[0m |
| [0m[1mKappa [0m [0m[1m 9.91%[0m |
| ────────────────── ────────── |
| Best mFscore 11.82% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 16.37% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4345 lr=4.12e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.4413 lr=4.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.4413 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 005 / 100 | Elapsed: 0:01:44[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.4413 |
| Val Loss 0.4460 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 11.46%[0m |
| [92m[1mmIoU [0m [92m[1m 11.69%[0m |
| [92m[1mmFscore [0m [92m[1m 18.82%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 22.89%[0m |
| [0m[1mmRecall [0m [0m[1m 41.25%[0m |
| [0m[1mKappa [0m [0m[1m 10.16%[0m |
| ────────────────── ────────── |
| Best mFscore 16.37% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 18.82% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4351 lr=5.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.4154 lr=5.00e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.4154 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 006 / 100 | Elapsed: 0:02:04[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.4154 |
| Val Loss 0.4360 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 12.97%[0m |
| [92m[1mmIoU [0m [92m[1m 10.54%[0m |
| [92m[1mmFscore [0m [92m[1m 17.72%[0m |
| [0m[1mmPrecision [0m [0m[1m 25.01%[0m |
| [0m[1mmRecall [0m [0m[1m 42.90%[0m |
| [0m[1mKappa [0m [0m[1m 11.14%[0m |
| ────────────────── ────────── |
| Best mFscore 18.82% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3873 lr=5.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3746 lr=5.00e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3746 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 007 / 100 | Elapsed: 0:02:23[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3746 |
| Val Loss 0.4288 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 16.28%[0m |
| [92m[1mmIoU [0m [92m[1m 13.62%[0m |
| [92m[1mmFscore [0m [92m[1m 22.34%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 27.74%[0m |
| [0m[1mmRecall [0m [0m[1m 47.86%[0m |
| [0m[1mKappa [0m [0m[1m 14.36%[0m |
| ────────────────── ────────── |
| Best mFscore 18.82% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 22.34% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3634 lr=4.99e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3566 lr=4.99e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3566 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 008 / 100 | Elapsed: 0:02:42[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3566 |
| Val Loss 0.3818 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 19.72%[0m |
| [92m[1mmIoU [0m [92m[1m 15.32%[0m |
| [92m[1mmFscore [0m [92m[1m 25.11%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 28.64%[0m |
| [0m[1mmRecall [0m [0m[1m 51.72%[0m |
| [0m[1mKappa [0m [0m[1m 17.29%[0m |
| ────────────────── ────────── |
| Best mFscore 22.34% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 25.11% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3313 lr=4.99e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3315 lr=4.98e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3315 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 009 / 100 | Elapsed: 0:03:02[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3315 |
| Val Loss 0.3572 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 23.30%[0m |
| [92m[1mmIoU [0m [92m[1m 19.39%[0m |
| [92m[1mmFscore [0m [92m[1m 30.21%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 33.87%[0m |
| [0m[1mmRecall [0m [0m[1m 53.58%[0m |
| [0m[1mKappa [0m [0m[1m 19.86%[0m |
| ────────────────── ────────── |
| Best mFscore 25.11% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 30.21% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3223 lr=4.98e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3238 lr=4.97e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3238 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 010 / 100 | Elapsed: 0:03:21[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3238 |
| Val Loss 0.4939 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 20.27%[0m |
| [92m[1mmIoU [0m [92m[1m 18.65%[0m |
| [92m[1mmFscore [0m [92m[1m 29.25%[0m |
| [0m[1mmPrecision [0m [0m[1m 34.12%[0m |
| [0m[1mmRecall [0m [0m[1m 51.13%[0m |
| [0m[1mKappa [0m [0m[1m 17.52%[0m |
| ────────────────── ────────── |
| Best mFscore 30.21% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3179 lr=4.97e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3157 lr=4.96e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3157 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 011 / 100 | Elapsed: 0:03:40[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3157 |
| Val Loss 0.3661 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 24.42%[0m |
| [92m[1mmIoU [0m [92m[1m 20.25%[0m |
| [92m[1mmFscore [0m [92m[1m 30.65%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 32.08%[0m |
| [0m[1mmRecall [0m [0m[1m 54.51%[0m |
| [0m[1mKappa [0m [0m[1m 21.51%[0m |
| ────────────────── ────────── |
| Best mFscore 30.21% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 30.65% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3048 lr=4.95e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3062 lr=4.94e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3062 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 012 / 100 | Elapsed: 0:04:00[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3062 |
| Val Loss 0.3400 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 25.10%[0m |
| [92m[1mmIoU [0m [92m[1m 21.31%[0m |
| [92m[1mmFscore [0m [92m[1m 31.92%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 35.16%[0m |
| [0m[1mmRecall [0m [0m[1m 54.92%[0m |
| [0m[1mKappa [0m [0m[1m 22.04%[0m |
| ────────────────── ────────── |
| Best mFscore 30.65% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 31.92% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2828 lr=4.93e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2964 lr=4.93e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2964 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 013 / 100 | Elapsed: 0:04:19[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2964 |
| Val Loss 0.3635 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 23.00%[0m |
| [92m[1mmIoU [0m [92m[1m 18.76%[0m |
| [92m[1mmFscore [0m [92m[1m 29.70%[0m |
| [0m[1mmPrecision [0m [0m[1m 32.78%[0m |
| [0m[1mmRecall [0m [0m[1m 53.91%[0m |
| [0m[1mKappa [0m [0m[1m 20.08%[0m |
| ────────────────── ────────── |
| Best mFscore 31.92% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2705 lr=4.91e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2743 lr=4.90e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2743 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 014 / 100 | Elapsed: 0:04:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2743 |
| Val Loss 0.3500 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 33.66%[0m |
| [92m[1mmIoU [0m [92m[1m 25.51%[0m |
| [92m[1mmFscore [0m [92m[1m 37.17%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 37.34%[0m |
| [0m[1mmRecall [0m [0m[1m 58.82%[0m |
| [0m[1mKappa [0m [0m[1m 29.27%[0m |
| ────────────────── ────────── |
| Best mFscore 31.92% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 37.17% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2488 lr=4.89e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2588 lr=4.88e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2588 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 015 / 100 | Elapsed: 0:04:58[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2588 |
| Val Loss 0.3663 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 35.18%[0m |
| [92m[1mmIoU [0m [92m[1m 25.93%[0m |
| [92m[1mmFscore [0m [92m[1m 38.04%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 38.24%[0m |
| [0m[1mmRecall [0m [0m[1m 58.74%[0m |
| [0m[1mKappa [0m [0m[1m 30.14%[0m |
| ────────────────── ────────── |
| Best mFscore 37.17% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 38.04% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2558 lr=4.87e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2591 lr=4.85e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2591 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 016 / 100 | Elapsed: 0:05:17[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2591 |
| Val Loss 0.3457 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 36.37%[0m |
| [92m[1mmIoU [0m [92m[1m 26.29%[0m |
| [92m[1mmFscore [0m [92m[1m 38.36%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 38.42%[0m |
| [0m[1mmRecall [0m [0m[1m 58.95%[0m |
| [0m[1mKappa [0m [0m[1m 31.04%[0m |
| ────────────────── ────────── |
| Best mFscore 38.04% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 38.36% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2384 lr=4.84e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2435 lr=4.82e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2435 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 017 / 100 | Elapsed: 0:05:37[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2435 |
| Val Loss 0.3454 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 35.70%[0m |
| [92m[1mmIoU [0m [92m[1m 25.54%[0m |
| [92m[1mmFscore [0m [92m[1m 37.95%[0m |
| [0m[1mmPrecision [0m [0m[1m 37.08%[0m |
| [0m[1mmRecall [0m [0m[1m 59.91%[0m |
| [0m[1mKappa [0m [0m[1m 30.87%[0m |
| ────────────────── ────────── |
| Best mFscore 38.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2461 lr=4.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2411 lr=4.79e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2411 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 018 / 100 | Elapsed: 0:05:56[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2411 |
| Val Loss 0.3398 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 31.91%[0m |
| [92m[1mmIoU [0m [92m[1m 23.07%[0m |
| [92m[1mmFscore [0m [92m[1m 35.07%[0m |
| [0m[1mmPrecision [0m [0m[1m 36.98%[0m |
| [0m[1mmRecall [0m [0m[1m 57.73%[0m |
| [0m[1mKappa [0m [0m[1m 27.23%[0m |
| ────────────────── ────────── |
| Best mFscore 38.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2304 lr=4.77e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2366 lr=4.76e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2366 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 019 / 100 | Elapsed: 0:06:15[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2366 |
| Val Loss 0.3246 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 36.11%[0m |
| [92m[1mmIoU [0m [92m[1m 26.92%[0m |
| [92m[1mmFscore [0m [92m[1m 39.24%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 39.42%[0m |
| [0m[1mmRecall [0m [0m[1m 60.58%[0m |
| [0m[1mKappa [0m [0m[1m 31.11%[0m |
| ────────────────── ────────── |
| Best mFscore 38.36% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 39.24% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2149 lr=4.74e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2167 lr=4.72e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2167 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 020 / 100 | Elapsed: 0:06:34[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2167 |
| Val Loss 0.3262 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 38.89%[0m |
| [92m[1mmIoU [0m [92m[1m 28.07%[0m |
| [92m[1mmFscore [0m [92m[1m 40.32%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 39.19%[0m |
| [0m[1mmRecall [0m [0m[1m 62.24%[0m |
| [0m[1mKappa [0m [0m[1m 33.84%[0m |
| ────────────────── ────────── |
| Best mFscore 39.24% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 40.32% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2207 lr=4.70e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2200 lr=4.68e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2200 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 021 / 100 | Elapsed: 0:06:54[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2200 |
| Val Loss 0.3503 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 38.78%[0m |
| [92m[1mmIoU [0m [92m[1m 26.68%[0m |
| [92m[1mmFscore [0m [92m[1m 39.30%[0m |
| [0m[1mmPrecision [0m [0m[1m 39.05%[0m |
| [0m[1mmRecall [0m [0m[1m 61.28%[0m |
| [0m[1mKappa [0m [0m[1m 33.05%[0m |
| ────────────────── ────────── |
| Best mFscore 40.32% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2162 lr=4.66e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2196 lr=4.64e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2196 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 022 / 100 | Elapsed: 0:07:13[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2196 |
| Val Loss 0.3382 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 37.54%[0m |
| [92m[1mmIoU [0m [92m[1m 28.62%[0m |
| [92m[1mmFscore [0m [92m[1m 40.93%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 40.25%[0m |
| [0m[1mmRecall [0m [0m[1m 61.95%[0m |
| [0m[1mKappa [0m [0m[1m 32.53%[0m |
| ────────────────── ────────── |
| Best mFscore 40.32% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 40.93% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2014 lr=4.62e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2098 lr=4.60e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2098 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 023 / 100 | Elapsed: 0:07:33[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2098 |
| Val Loss 0.3444 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 43.21%[0m |
| [92m[1mmIoU [0m [92m[1m 30.50%[0m |
| [92m[1mmFscore [0m [92m[1m 43.48%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 41.36%[0m |
| [0m[1mmRecall [0m [0m[1m 63.18%[0m |
| [0m[1mKappa [0m [0m[1m 37.21%[0m |
| ────────────────── ────────── |
| Best mFscore 40.93% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 43.48% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2084 lr=4.57e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1991 lr=4.55e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1991 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 024 / 100 | Elapsed: 0:07:52[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1991 |
| Val Loss 0.3626 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 41.44%[0m |
| [92m[1mmIoU [0m [92m[1m 28.87%[0m |
| [92m[1mmFscore [0m [92m[1m 41.47%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.84%[0m |
| [0m[1mmRecall [0m [0m[1m 62.44%[0m |
| [0m[1mKappa [0m [0m[1m 35.46%[0m |
| ────────────────── ────────── |
| Best mFscore 43.48% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1728 lr=4.53e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1908 lr=4.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1908 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 025 / 100 | Elapsed: 0:08:11[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1908 |
| Val Loss 0.3318 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 43.43%[0m |
| [92m[1mmIoU [0m [92m[1m 29.78%[0m |
| [92m[1mmFscore [0m [92m[1m 42.30%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.99%[0m |
| [0m[1mmRecall [0m [0m[1m 63.52%[0m |
| [0m[1mKappa [0m [0m[1m 37.29%[0m |
| ────────────────── ────────── |
| Best mFscore 43.48% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1853 lr=4.48e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1896 lr=4.45e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1896 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 026 / 100 | Elapsed: 0:08:30[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1896 |
| Val Loss 0.3496 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 45.11%[0m |
| [92m[1mmIoU [0m [92m[1m 30.53%[0m |
| [92m[1mmFscore [0m [92m[1m 43.46%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.88%[0m |
| [0m[1mmRecall [0m [0m[1m 63.42%[0m |
| [0m[1mKappa [0m [0m[1m 39.25%[0m |
| ────────────────── ────────── |
| Best mFscore 43.48% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1790 lr=4.43e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1821 lr=4.40e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1821 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 027 / 100 | Elapsed: 0:08:49[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1821 |
| Val Loss 0.3569 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.08%[0m |
| [92m[1mmIoU [0m [92m[1m 32.32%[0m |
| [92m[1mmFscore [0m [92m[1m 45.18%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 42.33%[0m |
| [0m[1mmRecall [0m [0m[1m 65.31%[0m |
| [0m[1mKappa [0m [0m[1m 40.82%[0m |
| ────────────────── ────────── |
| Best mFscore 43.48% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 45.18% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1772 lr=4.37e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1739 lr=4.35e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1739 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 028 / 100 | Elapsed: 0:09:09[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1739 |
| Val Loss 0.3843 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 42.67%[0m |
| [92m[1mmIoU [0m [92m[1m 29.26%[0m |
| [92m[1mmFscore [0m [92m[1m 42.04%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.22%[0m |
| [0m[1mmRecall [0m [0m[1m 63.82%[0m |
| [0m[1mKappa [0m [0m[1m 36.80%[0m |
| ────────────────── ────────── |
| Best mFscore 45.18% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1681 lr=4.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1679 lr=4.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1679 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 029 / 100 | Elapsed: 0:09:28[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1679 |
| Val Loss 0.3706 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.12%[0m |
| [92m[1mmIoU [0m [92m[1m 31.52%[0m |
| [92m[1mmFscore [0m [92m[1m 44.58%[0m |
| [0m[1mmPrecision [0m [0m[1m 41.95%[0m |
| [0m[1mmRecall [0m [0m[1m 65.10%[0m |
| [0m[1mKappa [0m [0m[1m 40.74%[0m |
| ────────────────── ────────── |
| Best mFscore 45.18% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1685 lr=4.26e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1686 lr=4.23e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1686 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 030 / 100 | Elapsed: 0:09:47[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1686 |
| Val Loss 0.3594 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 46.44%[0m |
| [92m[1mmIoU [0m [92m[1m 32.35%[0m |
| [92m[1mmFscore [0m [92m[1m 45.28%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.05%[0m |
| [0m[1mmRecall [0m [0m[1m 64.56%[0m |
| [0m[1mKappa [0m [0m[1m 39.89%[0m |
| ────────────────── ────────── |
| Best mFscore 45.18% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 45.28% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1712 lr=4.20e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1736 lr=4.17e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1736 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 031 / 100 | Elapsed: 0:10:06[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1736 |
| Val Loss 0.3546 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 48.16%[0m |
| [92m[1mmIoU [0m [92m[1m 33.86%[0m |
| [92m[1mmFscore [0m [92m[1m 46.67%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.05%[0m |
| [0m[1mmRecall [0m [0m[1m 64.95%[0m |
| [0m[1mKappa [0m [0m[1m 41.89%[0m |
| ────────────────── ────────── |
| Best mFscore 45.28% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 46.67% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1527 lr=4.14e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1564 lr=4.11e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1564 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 032 / 100 | Elapsed: 0:10:26[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1564 |
| Val Loss 0.3501 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 50.07%[0m |
| [92m[1mmIoU [0m [92m[1m 32.66%[0m |
| [92m[1mmFscore [0m [92m[1m 46.03%[0m |
| [0m[1mmPrecision [0m [0m[1m 42.27%[0m |
| [0m[1mmRecall [0m [0m[1m 67.13%[0m |
| [0m[1mKappa [0m [0m[1m 43.47%[0m |
| ────────────────── ────────── |
| Best mFscore 46.67% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1519 lr=4.08e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1603 lr=4.05e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1603 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 033 / 100 | Elapsed: 0:10:45[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1603 |
| Val Loss 0.3458 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 44.27%[0m |
| [92m[1mmIoU [0m [92m[1m 30.29%[0m |
| [92m[1mmFscore [0m [92m[1m 43.05%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.88%[0m |
| [0m[1mmRecall [0m [0m[1m 64.84%[0m |
| [0m[1mKappa [0m [0m[1m 38.14%[0m |
| ────────────────── ────────── |
| Best mFscore 46.67% |
| Val Time 4.1s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1451 lr=4.01e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1504 lr=3.98e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1504 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 034 / 100 | Elapsed: 0:11:04[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1504 |
| Val Loss 0.3515 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 50.52%[0m |
| [92m[1mmIoU [0m [92m[1m 33.69%[0m |
| [92m[1mmFscore [0m [92m[1m 46.89%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 43.23%[0m |
| [0m[1mmRecall [0m [0m[1m 67.19%[0m |
| [0m[1mKappa [0m [0m[1m 43.95%[0m |
| ────────────────── ────────── |
| Best mFscore 46.67% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 46.89% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1518 lr=3.95e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1522 lr=3.92e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1522 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 035 / 100 | Elapsed: 0:11:23[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1522 |
| Val Loss 0.3732 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 49.67%[0m |
| [92m[1mmIoU [0m [92m[1m 32.98%[0m |
| [92m[1mmFscore [0m [92m[1m 46.40%[0m |
| [0m[1mmPrecision [0m [0m[1m 42.61%[0m |
| [0m[1mmRecall [0m [0m[1m 66.09%[0m |
| [0m[1mKappa [0m [0m[1m 42.94%[0m |
| ────────────────── ────────── |
| Best mFscore 46.89% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1399 lr=3.88e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1429 lr=3.85e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1429 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 036 / 100 | Elapsed: 0:11:42[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1429 |
| Val Loss 0.3679 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 51.68%[0m |
| [92m[1mmIoU [0m [92m[1m 32.95%[0m |
| [92m[1mmFscore [0m [92m[1m 46.12%[0m |
| [0m[1mmPrecision [0m [0m[1m 42.65%[0m |
| [0m[1mmRecall [0m [0m[1m 65.51%[0m |
| [0m[1mKappa [0m [0m[1m 44.85%[0m |
| ────────────────── ────────── |
| Best mFscore 46.89% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1415 lr=3.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1439 lr=3.78e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1439 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 037 / 100 | Elapsed: 0:12:01[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1439 |
| Val Loss 0.3780 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.48%[0m |
| [92m[1mmIoU [0m [92m[1m 35.25%[0m |
| [92m[1mmFscore [0m [92m[1m 48.39%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.26%[0m |
| [0m[1mmRecall [0m [0m[1m 67.77%[0m |
| [0m[1mKappa [0m [0m[1m 46.64%[0m |
| ────────────────── ────────── |
| Best mFscore 46.89% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 48.39% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1412 lr=3.74e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1405 lr=3.71e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1405 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 038 / 100 | Elapsed: 0:12:21[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1405 |
| Val Loss 0.3848 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 54.33%[0m |
| [92m[1mmIoU [0m [92m[1m 36.00%[0m |
| [92m[1mmFscore [0m [92m[1m 49.48%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.53%[0m |
| [0m[1mmRecall [0m [0m[1m 67.41%[0m |
| [0m[1mKappa [0m [0m[1m 47.35%[0m |
| ────────────────── ────────── |
| Best mFscore 48.39% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 49.48% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1412 lr=3.67e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1344 lr=3.63e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1344 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 039 / 100 | Elapsed: 0:12:40[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1344 |
| Val Loss 0.3907 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.11%[0m |
| [92m[1mmIoU [0m [92m[1m 35.15%[0m |
| [92m[1mmFscore [0m [92m[1m 48.50%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.92%[0m |
| [0m[1mmRecall [0m [0m[1m 67.28%[0m |
| [0m[1mKappa [0m [0m[1m 46.52%[0m |
| ────────────────── ────────── |
| Best mFscore 49.48% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1344 lr=3.59e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1321 lr=3.56e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1321 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 040 / 100 | Elapsed: 0:12:59[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1321 |
| Val Loss 0.3992 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.46%[0m |
| [92m[1mmIoU [0m [92m[1m 37.35%[0m |
| [92m[1mmFscore [0m [92m[1m 50.77%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 46.10%[0m |
| [0m[1mmRecall [0m [0m[1m 67.89%[0m |
| [0m[1mKappa [0m [0m[1m 50.46%[0m |
| ────────────────── ────────── |
| Best mFscore 49.48% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 50.77% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1276 lr=3.52e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1293 lr=3.49e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1293 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 041 / 100 | Elapsed: 0:13:19[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1293 |
| Val Loss 0.3848 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 54.73%[0m |
| [92m[1mmIoU [0m [92m[1m 36.39%[0m |
| [92m[1mmFscore [0m [92m[1m 49.61%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.85%[0m |
| [0m[1mmRecall [0m [0m[1m 66.97%[0m |
| [0m[1mKappa [0m [0m[1m 47.69%[0m |
| ────────────────── ────────── |
| Best mFscore 50.77% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1273 lr=3.44e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1270 lr=3.41e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1270 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 042 / 100 | Elapsed: 0:13:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1270 |
| Val Loss 0.3750 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 55.64%[0m |
| [92m[1mmIoU [0m [92m[1m 35.21%[0m |
| [92m[1mmFscore [0m [92m[1m 48.44%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.87%[0m |
| [0m[1mmRecall [0m [0m[1m 67.42%[0m |
| [0m[1mKappa [0m [0m[1m 48.58%[0m |
| ────────────────── ────────── |
| Best mFscore 50.77% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1221 lr=3.37e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1222 lr=3.33e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1222 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 043 / 100 | Elapsed: 0:13:57[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1222 |
| Val Loss 0.3959 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 56.69%[0m |
| [92m[1mmIoU [0m [92m[1m 37.62%[0m |
| [92m[1mmFscore [0m [92m[1m 50.84%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 47.06%[0m |
| [0m[1mmRecall [0m [0m[1m 67.33%[0m |
| [0m[1mKappa [0m [0m[1m 49.67%[0m |
| ────────────────── ────────── |
| Best mFscore 50.77% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 50.84% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1132 lr=3.29e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1168 lr=3.26e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1168 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 044 / 100 | Elapsed: 0:14:17[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1168 |
| Val Loss 0.3729 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.00%[0m |
| [92m[1mmIoU [0m [92m[1m 37.62%[0m |
| [92m[1mmFscore [0m [92m[1m 51.33%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 46.91%[0m |
| [0m[1mmRecall [0m [0m[1m 68.95%[0m |
| [0m[1mKappa [0m [0m[1m 49.88%[0m |
| ────────────────── ────────── |
| Best mFscore 50.84% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 51.33% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1133 lr=3.21e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1143 lr=3.18e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1143 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 045 / 100 | Elapsed: 0:14:36[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1143 |
| Val Loss 0.3968 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.42%[0m |
| [92m[1mmIoU [0m [92m[1m 34.87%[0m |
| [92m[1mmFscore [0m [92m[1m 48.38%[0m |
| [0m[1mmPrecision [0m [0m[1m 44.83%[0m |
| [0m[1mmRecall [0m [0m[1m 67.22%[0m |
| [0m[1mKappa [0m [0m[1m 46.25%[0m |
| ────────────────── ────────── |
| Best mFscore 51.33% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1106 lr=3.13e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1120 lr=3.10e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1120 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 046 / 100 | Elapsed: 0:14:55[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1120 |
| Val Loss 0.3958 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 54.21%[0m |
| [92m[1mmIoU [0m [92m[1m 36.09%[0m |
| [92m[1mmFscore [0m [92m[1m 49.78%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.07%[0m |
| [0m[1mmRecall [0m [0m[1m 67.22%[0m |
| [0m[1mKappa [0m [0m[1m 47.54%[0m |
| ────────────────── ────────── |
| Best mFscore 51.33% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1127 lr=3.05e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1088 lr=3.02e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1088 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 047 / 100 | Elapsed: 0:15:14[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1088 |
| Val Loss 0.3915 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.45%[0m |
| [92m[1mmIoU [0m [92m[1m 38.41%[0m |
| [92m[1mmFscore [0m [92m[1m 52.04%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 46.95%[0m |
| [0m[1mmRecall [0m [0m[1m 68.74%[0m |
| [0m[1mKappa [0m [0m[1m 50.43%[0m |
| ────────────────── ────────── |
| Best mFscore 51.33% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 52.04% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1080 lr=2.97e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1082 lr=2.94e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1082 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 048 / 100 | Elapsed: 0:15:34[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1082 |
| Val Loss 0.3998 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.54%[0m |
| [92m[1mmIoU [0m [92m[1m 37.00%[0m |
| [92m[1mmFscore [0m [92m[1m 50.56%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.29%[0m |
| [0m[1mmRecall [0m [0m[1m 69.24%[0m |
| [0m[1mKappa [0m [0m[1m 50.44%[0m |
| ────────────────── ────────── |
| Best mFscore 52.04% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1009 lr=2.89e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1009 lr=2.86e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1009 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 049 / 100 | Elapsed: 0:15:53[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1009 |
| Val Loss 0.4085 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.40%[0m |
| [92m[1mmIoU [0m [92m[1m 38.85%[0m |
| [92m[1mmFscore [0m [92m[1m 52.21%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 47.40%[0m |
| [0m[1mmRecall [0m [0m[1m 68.56%[0m |
| [0m[1mKappa [0m [0m[1m 53.08%[0m |
| ────────────────── ────────── |
| Best mFscore 52.04% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 52.21% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1008 lr=2.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0992 lr=2.78e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0992 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 050 / 100 | Elapsed: 0:16:12[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0992 |
| Val Loss 0.3974 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 58.35%[0m |
| [92m[1mmIoU [0m [92m[1m 37.89%[0m |
| [92m[1mmFscore [0m [92m[1m 51.47%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.21%[0m |
| [0m[1mmRecall [0m [0m[1m 68.91%[0m |
| [0m[1mKappa [0m [0m[1m 51.36%[0m |
| ────────────────── ────────── |
| Best mFscore 52.21% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0975 lr=2.73e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0994 lr=2.69e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0994 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 051 / 100 | Elapsed: 0:16:32[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0994 |
| Val Loss 0.4259 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.13%[0m |
| [92m[1mmIoU [0m [92m[1m 39.64%[0m |
| [92m[1mmFscore [0m [92m[1m 53.11%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 48.47%[0m |
| [0m[1mmRecall [0m [0m[1m 68.61%[0m |
| [0m[1mKappa [0m [0m[1m 52.85%[0m |
| ────────────────── ────────── |
| Best mFscore 52.21% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 53.11% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0977 lr=2.65e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1009 lr=2.61e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1009 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 052 / 100 | Elapsed: 0:16:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1009 |
| Val Loss 0.3958 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.97%[0m |
| [92m[1mmIoU [0m [92m[1m 37.36%[0m |
| [92m[1mmFscore [0m [92m[1m 50.86%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.88%[0m |
| [0m[1mmRecall [0m [0m[1m 68.86%[0m |
| [0m[1mKappa [0m [0m[1m 50.63%[0m |
| ────────────────── ────────── |
| Best mFscore 53.11% |
| Val Time 3.8s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0961 lr=2.57e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0967 lr=2.53e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0967 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 053 / 100 | Elapsed: 0:17:10[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0967 |
| Val Loss 0.4270 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 58.63%[0m |
| [92m[1mmIoU [0m [92m[1m 38.37%[0m |
| [92m[1mmFscore [0m [92m[1m 51.77%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.77%[0m |
| [0m[1mmRecall [0m [0m[1m 68.70%[0m |
| [0m[1mKappa [0m [0m[1m 51.60%[0m |
| ────────────────── ────────── |
| Best mFscore 53.11% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0935 lr=2.49e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0923 lr=2.45e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0923 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 054 / 100 | Elapsed: 0:17:29[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0923 |
| Val Loss 0.4446 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 62.65%[0m |
| [92m[1mmIoU [0m [92m[1m 40.61%[0m |
| [92m[1mmFscore [0m [92m[1m 54.42%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 48.98%[0m |
| [0m[1mmRecall [0m [0m[1m 69.51%[0m |
| [0m[1mKappa [0m [0m[1m 55.38%[0m |
| ────────────────── ────────── |
| Best mFscore 53.11% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 54.42% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0968 lr=2.41e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0932 lr=2.37e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0932 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 055 / 100 | Elapsed: 0:17:49[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0932 |
| Val Loss 0.4378 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.63%[0m |
| [92m[1mmIoU [0m [92m[1m 39.08%[0m |
| [92m[1mmFscore [0m [92m[1m 52.88%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.97%[0m |
| [0m[1mmRecall [0m [0m[1m 69.39%[0m |
| [0m[1mKappa [0m [0m[1m 53.60%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0898 lr=2.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0901 lr=2.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0901 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 056 / 100 | Elapsed: 0:18:08[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0901 |
| Val Loss 0.4299 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.19%[0m |
| [92m[1mmIoU [0m [92m[1m 39.49%[0m |
| [92m[1mmFscore [0m [92m[1m 52.92%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.69%[0m |
| [0m[1mmRecall [0m [0m[1m 69.17%[0m |
| [0m[1mKappa [0m [0m[1m 53.07%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0833 lr=2.24e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0862 lr=2.21e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0862 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 057 / 100 | Elapsed: 0:18:27[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0862 |
| Val Loss 0.4314 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 59.27%[0m |
| [92m[1mmIoU [0m [92m[1m 39.35%[0m |
| [92m[1mmFscore [0m [92m[1m 53.04%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.77%[0m |
| [0m[1mmRecall [0m [0m[1m 69.30%[0m |
| [0m[1mKappa [0m [0m[1m 52.28%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0874 lr=2.16e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0865 lr=2.13e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0865 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 058 / 100 | Elapsed: 0:18:46[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0865 |
| Val Loss 0.4204 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.71%[0m |
| [92m[1mmIoU [0m [92m[1m 40.25%[0m |
| [92m[1mmFscore [0m [92m[1m 53.72%[0m |
| [0m[1mmPrecision [0m [0m[1m 48.48%[0m |
| [0m[1mmRecall [0m [0m[1m 69.07%[0m |
| [0m[1mKappa [0m [0m[1m 54.61%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0842 lr=2.08e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0816 lr=2.05e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0816 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 059 / 100 | Elapsed: 0:19:05[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0816 |
| Val Loss 0.4484 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 59.79%[0m |
| [92m[1mmIoU [0m [92m[1m 39.35%[0m |
| [92m[1mmFscore [0m [92m[1m 52.81%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.52%[0m |
| [0m[1mmRecall [0m [0m[1m 69.22%[0m |
| [0m[1mKappa [0m [0m[1m 52.76%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0819 lr=2.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0835 lr=1.97e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0835 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 060 / 100 | Elapsed: 0:19:24[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0835 |
| Val Loss 0.4536 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.31%[0m |
| [92m[1mmIoU [0m [92m[1m 38.81%[0m |
| [92m[1mmFscore [0m [92m[1m 52.35%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.66%[0m |
| [0m[1mmRecall [0m [0m[1m 69.09%[0m |
| [0m[1mKappa [0m [0m[1m 54.10%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0791 lr=1.92e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0813 lr=1.89e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0813 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 061 / 100 | Elapsed: 0:19:43[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0813 |
| Val Loss 0.4746 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 62.54%[0m |
| [92m[1mmIoU [0m [92m[1m 40.20%[0m |
| [92m[1mmFscore [0m [92m[1m 53.85%[0m |
| [0m[1mmPrecision [0m [0m[1m 48.24%[0m |
| [0m[1mmRecall [0m [0m[1m 68.92%[0m |
| [0m[1mKappa [0m [0m[1m 55.34%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0771 lr=1.84e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0784 lr=1.81e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0784 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 062 / 100 | Elapsed: 0:20:02[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0784 |
| Val Loss 0.4353 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.49%[0m |
| [92m[1mmIoU [0m [92m[1m 40.74%[0m |
| [92m[1mmFscore [0m [92m[1m 54.45%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 49.05%[0m |
| [0m[1mmRecall [0m [0m[1m 70.05%[0m |
| [0m[1mKappa [0m [0m[1m 54.40%[0m |
| ────────────────── ────────── |
| Best mFscore 54.42% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 54.45% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0775 lr=1.77e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0755 lr=1.73e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0755 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 063 / 100 | Elapsed: 0:20:22[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0755 |
| Val Loss 0.4359 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.09%[0m |
| [92m[1mmIoU [0m [92m[1m 41.52%[0m |
| [92m[1mmFscore [0m [92m[1m 55.15%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 49.72%[0m |
| [0m[1mmRecall [0m [0m[1m 69.88%[0m |
| [0m[1mKappa [0m [0m[1m 55.83%[0m |
| ────────────────── ────────── |
| Best mFscore 54.45% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 55.15% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0743 lr=1.69e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0749 lr=1.66e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0749 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 064 / 100 | Elapsed: 0:20:41[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0749 |
| Val Loss 0.4632 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.96%[0m |
| [92m[1mmIoU [0m [92m[1m 41.16%[0m |
| [92m[1mmFscore [0m [92m[1m 54.85%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.39%[0m |
| [0m[1mmRecall [0m [0m[1m 69.95%[0m |
| [0m[1mKappa [0m [0m[1m 54.89%[0m |
| ────────────────── ────────── |
| Best mFscore 55.15% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0728 lr=1.61e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0731 lr=1.58e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0731 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 065 / 100 | Elapsed: 0:21:00[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0731 |
| Val Loss 0.4885 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.22%[0m |
| [92m[1mmIoU [0m [92m[1m 41.79%[0m |
| [92m[1mmFscore [0m [92m[1m 55.52%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 49.96%[0m |
| [0m[1mmRecall [0m [0m[1m 69.51%[0m |
| [0m[1mKappa [0m [0m[1m 56.96%[0m |
| ────────────────── ────────── |
| Best mFscore 55.15% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 55.52% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0719 lr=1.54e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0703 lr=1.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0703 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 066 / 100 | Elapsed: 0:21:20[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0703 |
| Val Loss 0.4809 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.86%[0m |
| [92m[1mmIoU [0m [92m[1m 42.09%[0m |
| [92m[1mmFscore [0m [92m[1m 55.91%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.51%[0m |
| [0m[1mmRecall [0m [0m[1m 69.62%[0m |
| [0m[1mKappa [0m [0m[1m 56.73%[0m |
| ────────────────── ────────── |
| Best mFscore 55.52% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 55.91% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0698 lr=1.47e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0703 lr=1.43e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0703 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 067 / 100 | Elapsed: 0:21:39[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0703 |
| Val Loss 0.4813 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.77%[0m |
| [92m[1mmIoU [0m [92m[1m 41.48%[0m |
| [92m[1mmFscore [0m [92m[1m 55.14%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.53%[0m |
| [0m[1mmRecall [0m [0m[1m 69.48%[0m |
| [0m[1mKappa [0m [0m[1m 56.58%[0m |
| ────────────────── ────────── |
| Best mFscore 55.91% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0688 lr=1.39e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0692 lr=1.36e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0692 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 068 / 100 | Elapsed: 0:21:58[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0692 |
| Val Loss 0.4722 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.56%[0m |
| [92m[1mmIoU [0m [92m[1m 41.42%[0m |
| [92m[1mmFscore [0m [92m[1m 55.15%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.44%[0m |
| [0m[1mmRecall [0m [0m[1m 69.62%[0m |
| [0m[1mKappa [0m [0m[1m 57.36%[0m |
| ────────────────── ────────── |
| Best mFscore 55.91% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0709 lr=1.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0710 lr=1.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0710 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 069 / 100 | Elapsed: 0:22:18[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0710 |
| Val Loss 0.5008 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.58%[0m |
| [92m[1mmIoU [0m [92m[1m 42.71%[0m |
| [92m[1mmFscore [0m [92m[1m 56.36%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.34%[0m |
| [0m[1mmRecall [0m [0m[1m 68.96%[0m |
| [0m[1mKappa [0m [0m[1m 57.35%[0m |
| ────────────────── ────────── |
| Best mFscore 55.91% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 56.36% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0700 lr=1.25e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0680 lr=1.22e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0680 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 070 / 100 | Elapsed: 0:22:37[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0680 |
| Val Loss 0.4836 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.47%[0m |
| [92m[1mmIoU [0m [92m[1m 41.69%[0m |
| [92m[1mmFscore [0m [92m[1m 55.33%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.68%[0m |
| [0m[1mmRecall [0m [0m[1m 69.55%[0m |
| [0m[1mKappa [0m [0m[1m 57.29%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0646 lr=1.18e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0654 lr=1.15e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0654 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 071 / 100 | Elapsed: 0:22:56[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0654 |
| Val Loss 0.4857 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.42%[0m |
| [92m[1mmIoU [0m [92m[1m 41.56%[0m |
| [92m[1mmFscore [0m [92m[1m 55.33%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.60%[0m |
| [0m[1mmRecall [0m [0m[1m 69.54%[0m |
| [0m[1mKappa [0m [0m[1m 57.23%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0628 lr=1.12e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0634 lr=1.09e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0634 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 072 / 100 | Elapsed: 0:23:15[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0634 |
| Val Loss 0.5177 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.73%[0m |
| [92m[1mmIoU [0m [92m[1m 41.53%[0m |
| [92m[1mmFscore [0m [92m[1m 55.32%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.40%[0m |
| [0m[1mmRecall [0m [0m[1m 69.43%[0m |
| [0m[1mKappa [0m [0m[1m 57.56%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0661 lr=1.05e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0662 lr=1.02e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0662 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 073 / 100 | Elapsed: 0:23:34[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0662 |
| Val Loss 0.4972 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.26%[0m |
| [92m[1mmIoU [0m [92m[1m 41.56%[0m |
| [92m[1mmFscore [0m [92m[1m 55.31%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.71%[0m |
| [0m[1mmRecall [0m [0m[1m 69.58%[0m |
| [0m[1mKappa [0m [0m[1m 57.10%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0659 lr=9.88e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0634 lr=9.61e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0634 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 074 / 100 | Elapsed: 0:23:54[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0634 |
| Val Loss 0.4917 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.06%[0m |
| [92m[1mmIoU [0m [92m[1m 41.66%[0m |
| [92m[1mmFscore [0m [92m[1m 55.28%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.68%[0m |
| [0m[1mmRecall [0m [0m[1m 69.53%[0m |
| [0m[1mKappa [0m [0m[1m 56.90%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0621 lr=9.26e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0621 lr=8.99e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0621 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 075 / 100 | Elapsed: 0:24:13[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0621 |
| Val Loss 0.5098 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.60%[0m |
| [92m[1mmIoU [0m [92m[1m 42.00%[0m |
| [92m[1mmFscore [0m [92m[1m 55.72%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.15%[0m |
| [0m[1mmRecall [0m [0m[1m 69.70%[0m |
| [0m[1mKappa [0m [0m[1m 57.43%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0605 lr=8.66e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0614 lr=8.40e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0614 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 076 / 100 | Elapsed: 0:24:32[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0614 |
| Val Loss 0.5125 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.44%[0m |
| [92m[1mmIoU [0m [92m[1m 41.75%[0m |
| [92m[1mmFscore [0m [92m[1m 55.46%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.77%[0m |
| [0m[1mmRecall [0m [0m[1m 69.98%[0m |
| [0m[1mKappa [0m [0m[1m 57.29%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0584 lr=8.08e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0603 lr=7.83e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0603 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 077 / 100 | Elapsed: 0:24:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0603 |
| Val Loss 0.5269 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.11%[0m |
| [92m[1mmIoU [0m [92m[1m 41.97%[0m |
| [92m[1mmFscore [0m [92m[1m 55.88%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.10%[0m |
| [0m[1mmRecall [0m [0m[1m 69.53%[0m |
| [0m[1mKappa [0m [0m[1m 57.90%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0604 lr=7.52e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0592 lr=7.27e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0592 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 078 / 100 | Elapsed: 0:25:10[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0592 |
| Val Loss 0.5129 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.78%[0m |
| [92m[1mmIoU [0m [92m[1m 41.59%[0m |
| [92m[1mmFscore [0m [92m[1m 55.45%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.58%[0m |
| [0m[1mmRecall [0m [0m[1m 69.76%[0m |
| [0m[1mKappa [0m [0m[1m 57.65%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0585 lr=6.97e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0580 lr=6.74e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0580 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 079 / 100 | Elapsed: 0:25:29[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0580 |
| Val Loss 0.5199 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.33%[0m |
| [92m[1mmIoU [0m [92m[1m 42.12%[0m |
| [92m[1mmFscore [0m [92m[1m 55.91%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.11%[0m |
| [0m[1mmRecall [0m [0m[1m 69.68%[0m |
| [0m[1mKappa [0m [0m[1m 58.07%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.8s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0545 lr=6.45e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0582 lr=6.22e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0582 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 080 / 100 | Elapsed: 0:25:48[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0582 |
| Val Loss 0.5448 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.47%[0m |
| [92m[1mmIoU [0m [92m[1m 42.08%[0m |
| [92m[1mmFscore [0m [92m[1m 55.84%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.10%[0m |
| [0m[1mmRecall [0m [0m[1m 69.36%[0m |
| [0m[1mKappa [0m [0m[1m 58.16%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0561 lr=5.95e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0571 lr=5.73e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0571 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 081 / 100 | Elapsed: 0:26:07[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0571 |
| Val Loss 0.5364 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.85%[0m |
| [92m[1mmIoU [0m [92m[1m 42.72%[0m |
| [92m[1mmFscore [0m [92m[1m 56.46%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.99%[0m |
| [0m[1mmRecall [0m [0m[1m 69.32%[0m |
| [0m[1mKappa [0m [0m[1m 58.65%[0m |
| ────────────────── ────────── |
| Best mFscore 56.36% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 56.46% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0544 lr=5.47e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0560 lr=5.26e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0560 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 082 / 100 | Elapsed: 0:26:26[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0560 |
| Val Loss 0.5177 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.59%[0m |
| [92m[1mmIoU [0m [92m[1m 42.36%[0m |
| [92m[1mmFscore [0m [92m[1m 56.11%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.37%[0m |
| [0m[1mmRecall [0m [0m[1m 69.86%[0m |
| [0m[1mKappa [0m [0m[1m 58.41%[0m |
| ────────────────── ────────── |
| Best mFscore 56.46% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0583 lr=5.01e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0563 lr=4.81e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0563 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 083 / 100 | Elapsed: 0:26:45[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0563 |
| Val Loss 0.5201 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.33%[0m |
| [92m[1mmIoU [0m [92m[1m 42.63%[0m |
| [92m[1mmFscore [0m [92m[1m 56.43%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.83%[0m |
| [0m[1mmRecall [0m [0m[1m 69.52%[0m |
| [0m[1mKappa [0m [0m[1m 59.03%[0m |
| ────────────────── ────────── |
| Best mFscore 56.46% |
| Val Time 3.8s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0523 lr=4.57e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0549 lr=4.39e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0549 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 084 / 100 | Elapsed: 0:27:04[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0549 |
| Val Loss 0.5317 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.00%[0m |
| [92m[1mmIoU [0m [92m[1m 42.60%[0m |
| [92m[1mmFscore [0m [92m[1m 56.49%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.84%[0m |
| [0m[1mmRecall [0m [0m[1m 69.57%[0m |
| [0m[1mKappa [0m [0m[1m 58.76%[0m |
| ────────────────── ────────── |
| Best mFscore 56.46% |
| Val Time 3.8s |
|
|
| [92m[1m★ New best mFscore: 56.49% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0554 lr=4.16e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0544 lr=3.99e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0544 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 085 / 100 | Elapsed: 0:27:24[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0544 |
| Val Loss 0.5471 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.21%[0m |
| [92m[1mmIoU [0m [92m[1m 42.45%[0m |
| [92m[1mmFscore [0m [92m[1m 56.23%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.46%[0m |
| [0m[1mmRecall [0m [0m[1m 69.39%[0m |
| [0m[1mKappa [0m [0m[1m 59.02%[0m |
| ────────────────── ────────── |
| Best mFscore 56.49% |
| Val Time 3.8s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0574 lr=3.77e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0554 lr=3.61e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0554 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 086 / 100 | Elapsed: 0:27:43[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0554 |
| Val Loss 0.5406 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.34%[0m |
| [92m[1mmIoU [0m [92m[1m 42.76%[0m |
| [92m[1mmFscore [0m [92m[1m 56.63%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.85%[0m |
| [0m[1mmRecall [0m [0m[1m 69.80%[0m |
| [0m[1mKappa [0m [0m[1m 59.12%[0m |
| ────────────────── ────────── |
| Best mFscore 56.49% |
| Val Time 3.8s |
|
|
| [92m[1m★ New best mFscore: 56.63% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0560 lr=3.41e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0538 lr=3.26e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0538 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 087 / 100 | Elapsed: 0:28:02[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0538 |
| Val Loss 0.5529 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.47%[0m |
| [92m[1mmIoU [0m [92m[1m 42.55%[0m |
| [92m[1mmFscore [0m [92m[1m 56.38%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.49%[0m |
| [0m[1mmRecall [0m [0m[1m 69.53%[0m |
| [0m[1mKappa [0m [0m[1m 59.30%[0m |
| ────────────────── ────────── |
| Best mFscore 56.63% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0512 lr=3.07e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0535 lr=2.93e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0535 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 088 / 100 | Elapsed: 0:28:21[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0535 |
| Val Loss 0.5512 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.39%[0m |
| [92m[1mmIoU [0m [92m[1m 42.71%[0m |
| [92m[1mmFscore [0m [92m[1m 56.49%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.75%[0m |
| [0m[1mmRecall [0m [0m[1m 69.59%[0m |
| [0m[1mKappa [0m [0m[1m 59.16%[0m |
| ────────────────── ────────── |
| Best mFscore 56.63% |
| Val Time 3.8s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0530 lr=2.75e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0532 lr=2.62e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0532 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 089 / 100 | Elapsed: 0:28:40[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0532 |
| Val Loss 0.5414 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.74%[0m |
| [92m[1mmIoU [0m [92m[1m 42.30%[0m |
| [92m[1mmFscore [0m [92m[1m 56.11%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.40%[0m |
| [0m[1mmRecall [0m [0m[1m 69.53%[0m |
| [0m[1mKappa [0m [0m[1m 58.55%[0m |
| ────────────────── ────────── |
| Best mFscore 56.63% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0511 lr=2.46e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0529 lr=2.34e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0529 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 090 / 100 | Elapsed: 0:28:59[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0529 |
| Val Loss 0.5479 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.27%[0m |
| [92m[1mmIoU [0m [92m[1m 42.52%[0m |
| [92m[1mmFscore [0m [92m[1m 56.38%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.69%[0m |
| [0m[1mmRecall [0m [0m[1m 69.42%[0m |
| [0m[1mKappa [0m [0m[1m 59.05%[0m |
| ────────────────── ────────── |
| Best mFscore 56.63% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0531 lr=2.20e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0529 lr=2.09e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0529 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 091 / 100 | Elapsed: 0:29:18[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0529 |
| Val Loss 0.5639 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.82%[0m |
| [92m[1mmIoU [0m [92m[1m 42.97%[0m |
| [92m[1mmFscore [0m [92m[1m 56.82%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.16%[0m |
| [0m[1mmRecall [0m [0m[1m 69.42%[0m |
| [0m[1mKappa [0m [0m[1m 59.64%[0m |
| ────────────────── ────────── |
| Best mFscore 56.63% |
| Val Time 3.8s |
|
|
| [92m[1m★ New best mFscore: 56.82% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0512 lr=1.96e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0515 lr=1.86e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0515 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 092 / 100 | Elapsed: 0:29:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0515 |
| Val Loss 0.5644 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.79%[0m |
| [92m[1mmIoU [0m [92m[1m 42.78%[0m |
| [92m[1mmFscore [0m [92m[1m 56.65%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.89%[0m |
| [0m[1mmRecall [0m [0m[1m 69.41%[0m |
| [0m[1mKappa [0m [0m[1m 59.60%[0m |
| ────────────────── ────────── |
| Best mFscore 56.82% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0512 lr=1.75e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0521 lr=1.66e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0521 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 093 / 100 | Elapsed: 0:29:57[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0521 |
| Val Loss 0.5689 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.29%[0m |
| [92m[1mmIoU [0m [92m[1m 42.74%[0m |
| [92m[1mmFscore [0m [92m[1m 56.53%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.87%[0m |
| [0m[1mmRecall [0m [0m[1m 69.37%[0m |
| [0m[1mKappa [0m [0m[1m 59.11%[0m |
| ────────────────── ────────── |
| Best mFscore 56.82% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0527 lr=1.56e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0523 lr=1.49e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0523 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 094 / 100 | Elapsed: 0:30:16[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0523 |
| Val Loss 0.5571 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 67.02%[0m |
| [92m[1mmIoU [0m [92m[1m 42.83%[0m |
| [92m[1mmFscore [0m [92m[1m 56.70%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.89%[0m |
| [0m[1mmRecall [0m [0m[1m 69.48%[0m |
| [0m[1mKappa [0m [0m[1m 59.80%[0m |
| ────────────────── ────────── |
| Best mFscore 56.82% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0517 lr=1.40e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0518 lr=1.34e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0518 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 095 / 100 | Elapsed: 0:30:35[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0518 |
| Val Loss 0.5690 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.67%[0m |
| [92m[1mmIoU [0m [92m[1m 42.94%[0m |
| [92m[1mmFscore [0m [92m[1m 56.77%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.11%[0m |
| [0m[1mmRecall [0m [0m[1m 69.43%[0m |
| [0m[1mKappa [0m [0m[1m 59.48%[0m |
| ────────────────── ────────── |
| Best mFscore 56.82% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0502 lr=1.27e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0512 lr=1.22e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0512 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 096 / 100 | Elapsed: 0:30:54[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0512 |
| Val Loss 0.5629 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.68%[0m |
| [92m[1mmIoU [0m [92m[1m 43.03%[0m |
| [92m[1mmFscore [0m [92m[1m 56.85%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.21%[0m |
| [0m[1mmRecall [0m [0m[1m 69.48%[0m |
| [0m[1mKappa [0m [0m[1m 59.50%[0m |
| ────────────────── ────────── |
| Best mFscore 56.82% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 56.85% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0504 lr=1.16e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0508 lr=1.12e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0508 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 097 / 100 | Elapsed: 0:31:14[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0508 |
| Val Loss 0.5547 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.53%[0m |
| [92m[1mmIoU [0m [92m[1m 42.95%[0m |
| [92m[1mmFscore [0m [92m[1m 56.82%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.17%[0m |
| [0m[1mmRecall [0m [0m[1m 69.50%[0m |
| [0m[1mKappa [0m [0m[1m 59.35%[0m |
| ────────────────── ────────── |
| Best mFscore 56.85% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0519 lr=1.08e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0520 lr=1.05e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0520 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 098 / 100 | Elapsed: 0:31:33[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0520 |
| Val Loss 0.5641 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.86%[0m |
| [92m[1mmIoU [0m [92m[1m 43.04%[0m |
| [92m[1mmFscore [0m [92m[1m 56.86%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.21%[0m |
| [0m[1mmRecall [0m [0m[1m 69.36%[0m |
| [0m[1mKappa [0m [0m[1m 59.67%[0m |
| ────────────────── ────────── |
| Best mFscore 56.85% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 56.86% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0489 lr=1.03e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0506 lr=1.01e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0506 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 099 / 100 | Elapsed: 0:31:53[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0506 |
| Val Loss 0.5691 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.63%[0m |
| [92m[1mmIoU [0m [92m[1m 42.55%[0m |
| [92m[1mmFscore [0m [92m[1m 56.43%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.58%[0m |
| [0m[1mmRecall [0m [0m[1m 69.36%[0m |
| [0m[1mKappa [0m [0m[1m 59.44%[0m |
| ────────────────── ────────── |
| Best mFscore 56.86% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0510 lr=1.00e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0509 lr=1.00e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0509 time=0:00:14[0m |
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| [1mValidating...[0m |
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| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 100 / 100 | Elapsed: 0:32:12[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
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| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0509 |
| Val Loss 0.5763 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 67.47%[0m |
| [92m[1mmIoU [0m [92m[1m 43.20%[0m |
| [92m[1mmFscore [0m [92m[1m 57.09%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.42%[0m |
| [0m[1mmRecall [0m [0m[1m 69.28%[0m |
| [0m[1mKappa [0m [0m[1m 60.24%[0m |
| ────────────────── ────────── |
| Best mFscore 56.86% |
| Val Time 4.0s |
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| [92m[1m★ New best mFscore: 57.09% → saved best_model.pth[0m |
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| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
| [1m[96m Final Test Evaluation[0m |
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
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| [1mTestidating...[0m |
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| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Final Test Results[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
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| Metric Value |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.41%[0m |
| [92m[1mmIoU [0m [92m[1m 42.58%[0m |
| [92m[1mmFscore [0m [92m[1m 56.49%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.24%[0m |
| [0m[1mmRecall [0m [0m[1m 68.72%[0m |
| [0m[1mKappa [0m [0m[1m 58.99%[0m |
| ────────────────── ────────── |
| Test Loss 0.6047 |
| Total Time 0:32:19 |
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| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Per-Class IoU (Test)[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
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| Class IoU Bar |
| ────────────────────────────── ─────── ──────────────────── |
| [92mBackground 49.83% █████████[0m |
| [92mMeadow 55.29% ███████████[0m |
| [92mSoft winter wheat 73.37% ██████████████[0m |
| [92mCorn 74.60% ██████████████[0m |
| [92mWinter barley 56.94% ███████████[0m |
| [92mWinter rapeseed 72.90% ██████████████[0m |
| [92mSpring barley 33.97% ██████[0m |
| [92mSunflower 50.59% ██████████[0m |
| [92mGrapevine 34.60% ██████[0m |
| [92mBeet 73.78% ██████████████[0m |
| [93mWinter triticale 19.32% ███[0m |
| [92mWinter durum wheat 39.38% ███████[0m |
| [92mFruits vegetables flowers 21.97% ████[0m |
| [92mPotatoes 29.07% █████[0m |
| [93mLeguminous fodder 18.12% ███[0m |
| [92mSoybeans 59.49% ███████████[0m |
| [93mOrchard 13.14% ██[0m |
| [93mMixed cereal 13.02% ██[0m |
| [93mSorghum 19.68% ███[0m |
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| [92m[1mAll results saved to: ./work_dirs/fold5_small[0m |
| training_log.txt — full training log |
| log.json — per-epoch metrics |
| test_results.json — final test + per-class IoU |
| best_model.pth — best checkpoint (by val mFscore) |
| latest.pth — last epoch checkpoint |
| args.json — training configuration |
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