| /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 1 | 2026-04-17 03:43:18[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/fold1_v3 |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Building Datasets[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| PASTIS fold=1 split=train: 1452 patches (augment=True) |
| PASTIS fold=1 split=val: 494 patches (augment=False) |
| PASTIS fold=1 split=test: 487 patches (augment=False) |
|
|
| Train: 1452 Val: 494 Test: 487 |
| 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=1, sampled 300 patches): |
| Class Count Weight |
| ────────────────────────────── ────────── ──────── |
| Background 1870173 0.011 |
| Meadow 1009136 0.021 |
| Soft winter wheat 387319 0.053 |
| Corn 615155 0.034 |
| Winter barley 113032 0.183 |
| Winter rapeseed 82196 0.252 |
| Spring barley 38337 0.540 |
| Sunflower 34238 0.605 |
| Grapevine 53971 0.384 |
| Beet 42743 0.485 |
| Winter triticale 49575 0.418 |
| Winter durum wheat 2859 7.244 |
| Fruits vegetables flowers 21729 0.953 |
| Potatoes 8159 2.538 |
| Leguminous fodder 51195 0.405 |
| Soybeans 85622 0.242 |
| Orchard 6905 2.999 |
| Mixed cereal 31173 0.664 |
| Sorghum 21363 0.969 |
|
|
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
| [1m[96m Training | 100 epochs | model=small (~14M) | fold=1[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=1.0444 lr=5.90e-06 eta=0:00:10 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.8784 lr=9.82e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.8784 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: 0:00:32[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.8784 |
| Val Loss 0.6709 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 3.57%[0m |
| [92m[1mmIoU [0m [92m[1m 1.47%[0m |
| [92m[1mmFscore [0m [92m[1m 2.79%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 8.66%[0m |
| [0m[1mmRecall [0m [0m[1m 15.91%[0m |
| [0m[1mKappa [0m [0m[1m 2.39%[0m |
| ────────────────── ────────── |
| Best mFscore 0.00% |
| Val Time 11.6s |
|
|
| [92m[1m★ New best mFscore: 2.79% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.6183 lr=1.47e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.5839 lr=1.86e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.5839 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 002 / 100 | Elapsed: 0:00:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.5839 |
| Val Loss 0.5782 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 6.50%[0m |
| [92m[1mmIoU [0m [92m[1m 3.50%[0m |
| [92m[1mmFscore [0m [92m[1m 6.48%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 9.22%[0m |
| [0m[1mmRecall [0m [0m[1m 24.10%[0m |
| [0m[1mKappa [0m [0m[1m 5.13%[0m |
| ────────────────── ────────── |
| Best mFscore 2.79% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 6.48% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4820 lr=2.35e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.4662 lr=2.75e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.4662 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 003 / 100 | Elapsed: 0:01:11[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.4662 |
| Val Loss 0.4786 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 9.49%[0m |
| [92m[1mmIoU [0m [92m[1m 7.06%[0m |
| [92m[1mmFscore [0m [92m[1m 12.63%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 14.78%[0m |
| [0m[1mmRecall [0m [0m[1m 32.54%[0m |
| [0m[1mKappa [0m [0m[1m 7.98%[0m |
| ────────────────── ────────── |
| Best mFscore 6.48% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 12.63% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4565 lr=3.24e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.4309 lr=3.63e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.4309 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 004 / 100 | Elapsed: 0:01:30[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.4309 |
| Val Loss 0.4720 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 12.08%[0m |
| [92m[1mmIoU [0m [92m[1m 12.34%[0m |
| [92m[1mmFscore [0m [92m[1m 20.08%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 22.79%[0m |
| [0m[1mmRecall [0m [0m[1m 41.66%[0m |
| [0m[1mKappa [0m [0m[1m 10.76%[0m |
| ────────────────── ────────── |
| Best mFscore 12.63% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 20.08% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.4113 lr=4.12e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3998 lr=4.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3998 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 005 / 100 | Elapsed: 0:01:50[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3998 |
| Val Loss 0.4890 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 12.01%[0m |
| [92m[1mmIoU [0m [92m[1m 11.07%[0m |
| [92m[1mmFscore [0m [92m[1m 18.57%[0m |
| [0m[1mmPrecision [0m [0m[1m 21.99%[0m |
| [0m[1mmRecall [0m [0m[1m 39.34%[0m |
| [0m[1mKappa [0m [0m[1m 10.31%[0m |
| ────────────────── ────────── |
| Best mFscore 20.08% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3965 lr=5.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3881 lr=5.00e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3881 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 006 / 100 | Elapsed: 0:02:09[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3881 |
| Val Loss 0.4070 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 14.40%[0m |
| [92m[1mmIoU [0m [92m[1m 12.26%[0m |
| [92m[1mmFscore [0m [92m[1m 20.72%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 27.68%[0m |
| [0m[1mmRecall [0m [0m[1m 45.63%[0m |
| [0m[1mKappa [0m [0m[1m 12.80%[0m |
| ────────────────── ────────── |
| Best mFscore 20.08% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 20.72% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3516 lr=5.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3534 lr=5.00e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3534 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 007 / 100 | Elapsed: 0:02:29[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3534 |
| Val Loss 0.3764 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 14.16%[0m |
| [92m[1mmIoU [0m [92m[1m 14.18%[0m |
| [92m[1mmFscore [0m [92m[1m 22.70%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 30.07%[0m |
| [0m[1mmRecall [0m [0m[1m 46.73%[0m |
| [0m[1mKappa [0m [0m[1m 12.45%[0m |
| ────────────────── ────────── |
| Best mFscore 20.72% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 22.70% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3367 lr=4.99e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3303 lr=4.99e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3303 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 008 / 100 | Elapsed: 0:02:48[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3303 |
| Val Loss 0.3614 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 15.12%[0m |
| [92m[1mmIoU [0m [92m[1m 15.20%[0m |
| [92m[1mmFscore [0m [92m[1m 23.94%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 30.59%[0m |
| [0m[1mmRecall [0m [0m[1m 46.21%[0m |
| [0m[1mKappa [0m [0m[1m 13.14%[0m |
| ────────────────── ────────── |
| Best mFscore 22.70% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 23.94% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.3107 lr=4.99e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.3054 lr=4.98e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.3054 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 009 / 100 | Elapsed: 0:03:08[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.3054 |
| Val Loss 0.3804 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 18.76%[0m |
| [92m[1mmIoU [0m [92m[1m 16.87%[0m |
| [92m[1mmFscore [0m [92m[1m 26.62%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 30.47%[0m |
| [0m[1mmRecall [0m [0m[1m 52.01%[0m |
| [0m[1mKappa [0m [0m[1m 16.71%[0m |
| ────────────────── ────────── |
| Best mFscore 23.94% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 26.62% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2932 lr=4.98e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2959 lr=4.97e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2959 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 010 / 100 | Elapsed: 0:03:27[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2959 |
| Val Loss 0.3589 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 23.01%[0m |
| [92m[1mmIoU [0m [92m[1m 19.83%[0m |
| [92m[1mmFscore [0m [92m[1m 30.58%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 33.72%[0m |
| [0m[1mmRecall [0m [0m[1m 53.48%[0m |
| [0m[1mKappa [0m [0m[1m 20.36%[0m |
| ────────────────── ────────── |
| Best mFscore 26.62% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 30.58% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2732 lr=4.97e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2809 lr=4.96e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2809 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 011 / 100 | Elapsed: 0:03:47[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2809 |
| Val Loss 0.3557 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 22.92%[0m |
| [92m[1mmIoU [0m [92m[1m 19.11%[0m |
| [92m[1mmFscore [0m [92m[1m 29.75%[0m |
| [0m[1mmPrecision [0m [0m[1m 33.72%[0m |
| [0m[1mmRecall [0m [0m[1m 53.61%[0m |
| [0m[1mKappa [0m [0m[1m 19.97%[0m |
| ────────────────── ────────── |
| Best mFscore 30.58% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2701 lr=4.95e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2735 lr=4.94e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2735 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 012 / 100 | Elapsed: 0:04:06[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2735 |
| Val Loss 0.3768 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 20.09%[0m |
| [92m[1mmIoU [0m [92m[1m 17.77%[0m |
| [92m[1mmFscore [0m [92m[1m 27.92%[0m |
| [0m[1mmPrecision [0m [0m[1m 33.56%[0m |
| [0m[1mmRecall [0m [0m[1m 52.98%[0m |
| [0m[1mKappa [0m [0m[1m 17.48%[0m |
| ────────────────── ────────── |
| Best mFscore 30.58% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2604 lr=4.93e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2661 lr=4.93e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2661 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 013 / 100 | Elapsed: 0:04:25[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2661 |
| Val Loss 0.3192 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 25.54%[0m |
| [92m[1mmIoU [0m [92m[1m 22.96%[0m |
| [92m[1mmFscore [0m [92m[1m 34.00%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 36.87%[0m |
| [0m[1mmRecall [0m [0m[1m 56.18%[0m |
| [0m[1mKappa [0m [0m[1m 22.15%[0m |
| ────────────────── ────────── |
| Best mFscore 30.58% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 34.00% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2625 lr=4.91e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2612 lr=4.90e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2612 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 014 / 100 | Elapsed: 0:04:45[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2612 |
| Val Loss 0.3225 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 30.00%[0m |
| [92m[1mmIoU [0m [92m[1m 21.44%[0m |
| [92m[1mmFscore [0m [92m[1m 32.95%[0m |
| [0m[1mmPrecision [0m [0m[1m 35.42%[0m |
| [0m[1mmRecall [0m [0m[1m 55.13%[0m |
| [0m[1mKappa [0m [0m[1m 25.52%[0m |
| ────────────────── ────────── |
| Best mFscore 34.00% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2516 lr=4.89e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2459 lr=4.88e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2459 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 015 / 100 | Elapsed: 0:05:03[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2459 |
| Val Loss 0.3251 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 29.83%[0m |
| [92m[1mmIoU [0m [92m[1m 22.94%[0m |
| [92m[1mmFscore [0m [92m[1m 34.38%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 36.14%[0m |
| [0m[1mmRecall [0m [0m[1m 56.53%[0m |
| [0m[1mKappa [0m [0m[1m 25.89%[0m |
| ────────────────── ────────── |
| Best mFscore 34.00% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 34.38% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2263 lr=4.87e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2391 lr=4.85e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2391 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 016 / 100 | Elapsed: 0:05:23[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2391 |
| Val Loss 0.3981 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 27.88%[0m |
| [92m[1mmIoU [0m [92m[1m 23.12%[0m |
| [92m[1mmFscore [0m [92m[1m 34.49%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 36.08%[0m |
| [0m[1mmRecall [0m [0m[1m 57.16%[0m |
| [0m[1mKappa [0m [0m[1m 24.30%[0m |
| ────────────────── ────────── |
| Best mFscore 34.38% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 34.49% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2371 lr=4.84e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2282 lr=4.82e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2282 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 017 / 100 | Elapsed: 0:05:43[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2282 |
| Val Loss 0.3100 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 32.30%[0m |
| [92m[1mmIoU [0m [92m[1m 24.68%[0m |
| [92m[1mmFscore [0m [92m[1m 36.61%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 38.05%[0m |
| [0m[1mmRecall [0m [0m[1m 58.94%[0m |
| [0m[1mKappa [0m [0m[1m 27.72%[0m |
| ────────────────── ────────── |
| Best mFscore 34.49% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 36.61% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1989 lr=4.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2222 lr=4.79e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2222 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 018 / 100 | Elapsed: 0:06:02[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2222 |
| Val Loss 0.3232 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 28.50%[0m |
| [92m[1mmIoU [0m [92m[1m 24.17%[0m |
| [92m[1mmFscore [0m [92m[1m 35.55%[0m |
| [0m[1mmPrecision [0m [0m[1m 37.50%[0m |
| [0m[1mmRecall [0m [0m[1m 58.25%[0m |
| [0m[1mKappa [0m [0m[1m 24.93%[0m |
| ────────────────── ────────── |
| Best mFscore 36.61% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2172 lr=4.77e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2179 lr=4.76e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2179 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 019 / 100 | Elapsed: 0:06:22[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2179 |
| Val Loss 0.3206 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 37.06%[0m |
| [92m[1mmIoU [0m [92m[1m 25.46%[0m |
| [92m[1mmFscore [0m [92m[1m 37.63%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 37.21%[0m |
| [0m[1mmRecall [0m [0m[1m 60.87%[0m |
| [0m[1mKappa [0m [0m[1m 31.73%[0m |
| ────────────────── ────────── |
| Best mFscore 36.61% |
| Val Time 4.1s |
|
|
| [92m[1m★ New best mFscore: 37.63% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.2090 lr=4.74e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2090 lr=4.72e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2090 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 020 / 100 | Elapsed: 0:06:41[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2090 |
| Val Loss 0.3073 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 35.14%[0m |
| [92m[1mmIoU [0m [92m[1m 25.58%[0m |
| [92m[1mmFscore [0m [92m[1m 37.96%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 37.13%[0m |
| [0m[1mmRecall [0m [0m[1m 61.40%[0m |
| [0m[1mKappa [0m [0m[1m 30.10%[0m |
| ────────────────── ────────── |
| Best mFscore 37.63% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 37.96% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1897 lr=4.70e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.2006 lr=4.68e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.2006 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 021 / 100 | Elapsed: 0:07:01[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.2006 |
| Val Loss 0.3004 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 38.95%[0m |
| [92m[1mmIoU [0m [92m[1m 29.20%[0m |
| [92m[1mmFscore [0m [92m[1m 41.38%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 40.48%[0m |
| [0m[1mmRecall [0m [0m[1m 62.91%[0m |
| [0m[1mKappa [0m [0m[1m 33.49%[0m |
| ────────────────── ────────── |
| Best mFscore 37.96% |
| Val Time 4.1s |
|
|
| [92m[1m★ New best mFscore: 41.38% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1851 lr=4.66e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1907 lr=4.64e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1907 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 022 / 100 | Elapsed: 0:07:21[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1907 |
| Val Loss 0.3030 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 41.91%[0m |
| [92m[1mmIoU [0m [92m[1m 30.47%[0m |
| [92m[1mmFscore [0m [92m[1m 43.20%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 41.35%[0m |
| [0m[1mmRecall [0m [0m[1m 63.91%[0m |
| [0m[1mKappa [0m [0m[1m 35.86%[0m |
| ────────────────── ────────── |
| Best mFscore 41.38% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 43.20% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1830 lr=4.62e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1842 lr=4.60e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1842 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 023 / 100 | Elapsed: 0:07:40[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1842 |
| Val Loss 0.2982 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 43.19%[0m |
| [92m[1mmIoU [0m [92m[1m 29.35%[0m |
| [92m[1mmFscore [0m [92m[1m 42.28%[0m |
| [0m[1mmPrecision [0m [0m[1m 39.18%[0m |
| [0m[1mmRecall [0m [0m[1m 64.59%[0m |
| [0m[1mKappa [0m [0m[1m 37.18%[0m |
| ────────────────── ────────── |
| Best mFscore 43.20% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1737 lr=4.57e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1748 lr=4.55e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1748 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 024 / 100 | Elapsed: 0:07:59[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1748 |
| Val Loss 0.3056 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 36.71%[0m |
| [92m[1mmIoU [0m [92m[1m 28.66%[0m |
| [92m[1mmFscore [0m [92m[1m 40.94%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.46%[0m |
| [0m[1mmRecall [0m [0m[1m 62.47%[0m |
| [0m[1mKappa [0m [0m[1m 31.71%[0m |
| ────────────────── ────────── |
| Best mFscore 43.20% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1616 lr=4.53e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1646 lr=4.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1646 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 025 / 100 | Elapsed: 0:08:18[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1646 |
| Val Loss 0.3151 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 39.12%[0m |
| [92m[1mmIoU [0m [92m[1m 31.17%[0m |
| [92m[1mmFscore [0m [92m[1m 43.85%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 42.83%[0m |
| [0m[1mmRecall [0m [0m[1m 63.40%[0m |
| [0m[1mKappa [0m [0m[1m 34.04%[0m |
| ────────────────── ────────── |
| Best mFscore 43.20% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 43.85% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1653 lr=4.48e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1711 lr=4.45e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1711 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 026 / 100 | Elapsed: 0:08:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1711 |
| Val Loss 0.3080 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 41.34%[0m |
| [92m[1mmIoU [0m [92m[1m 29.55%[0m |
| [92m[1mmFscore [0m [92m[1m 42.01%[0m |
| [0m[1mmPrecision [0m [0m[1m 40.38%[0m |
| [0m[1mmRecall [0m [0m[1m 63.80%[0m |
| [0m[1mKappa [0m [0m[1m 35.84%[0m |
| ────────────────── ────────── |
| Best mFscore 43.85% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1696 lr=4.43e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1733 lr=4.40e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1733 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 027 / 100 | Elapsed: 0:08:57[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1733 |
| Val Loss 0.3106 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 44.61%[0m |
| [92m[1mmIoU [0m [92m[1m 31.69%[0m |
| [92m[1mmFscore [0m [92m[1m 44.41%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 42.21%[0m |
| [0m[1mmRecall [0m [0m[1m 65.78%[0m |
| [0m[1mKappa [0m [0m[1m 38.62%[0m |
| ────────────────── ────────── |
| Best mFscore 43.85% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 44.41% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1751 lr=4.37e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1698 lr=4.35e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1698 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 028 / 100 | Elapsed: 0:09:17[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1698 |
| Val Loss 0.2957 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 44.62%[0m |
| [92m[1mmIoU [0m [92m[1m 31.46%[0m |
| [92m[1mmFscore [0m [92m[1m 44.14%[0m |
| [0m[1mmPrecision [0m [0m[1m 42.54%[0m |
| [0m[1mmRecall [0m [0m[1m 65.16%[0m |
| [0m[1mKappa [0m [0m[1m 38.31%[0m |
| ────────────────── ────────── |
| Best mFscore 44.41% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1597 lr=4.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1598 lr=4.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1598 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 029 / 100 | Elapsed: 0:09:36[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1598 |
| Val Loss 0.2871 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.33%[0m |
| [92m[1mmIoU [0m [92m[1m 33.17%[0m |
| [92m[1mmFscore [0m [92m[1m 46.09%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 43.05%[0m |
| [0m[1mmRecall [0m [0m[1m 66.82%[0m |
| [0m[1mKappa [0m [0m[1m 41.21%[0m |
| ────────────────── ────────── |
| Best mFscore 44.41% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 46.09% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1439 lr=4.26e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1515 lr=4.23e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1515 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 030 / 100 | Elapsed: 0:09:56[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1515 |
| Val Loss 0.3012 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 49.35%[0m |
| [92m[1mmIoU [0m [92m[1m 34.48%[0m |
| [92m[1mmFscore [0m [92m[1m 47.39%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.69%[0m |
| [0m[1mmRecall [0m [0m[1m 66.42%[0m |
| [0m[1mKappa [0m [0m[1m 43.09%[0m |
| ────────────────── ────────── |
| Best mFscore 46.09% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 47.39% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1441 lr=4.20e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1474 lr=4.17e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1474 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 031 / 100 | Elapsed: 0:10:15[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1474 |
| Val Loss 0.2963 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 50.99%[0m |
| [92m[1mmIoU [0m [92m[1m 34.85%[0m |
| [92m[1mmFscore [0m [92m[1m 48.05%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 44.70%[0m |
| [0m[1mmRecall [0m [0m[1m 66.61%[0m |
| [0m[1mKappa [0m [0m[1m 44.43%[0m |
| ────────────────── ────────── |
| Best mFscore 47.39% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 48.05% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1411 lr=4.14e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1438 lr=4.11e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1438 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 032 / 100 | Elapsed: 0:10:35[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1438 |
| Val Loss 0.3137 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.58%[0m |
| [92m[1mmIoU [0m [92m[1m 33.22%[0m |
| [92m[1mmFscore [0m [92m[1m 46.30%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.25%[0m |
| [0m[1mmRecall [0m [0m[1m 66.18%[0m |
| [0m[1mKappa [0m [0m[1m 41.36%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1320 lr=4.08e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1342 lr=4.05e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1342 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 033 / 100 | Elapsed: 0:10:54[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1342 |
| Val Loss 0.3007 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.79%[0m |
| [92m[1mmIoU [0m [92m[1m 33.90%[0m |
| [92m[1mmFscore [0m [92m[1m 46.74%[0m |
| [0m[1mmPrecision [0m [0m[1m 44.04%[0m |
| [0m[1mmRecall [0m [0m[1m 66.65%[0m |
| [0m[1mKappa [0m [0m[1m 41.60%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1330 lr=4.01e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1293 lr=3.98e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1293 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 034 / 100 | Elapsed: 0:11:13[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1293 |
| Val Loss 0.2961 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 50.05%[0m |
| [92m[1mmIoU [0m [92m[1m 33.85%[0m |
| [92m[1mmFscore [0m [92m[1m 46.97%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.59%[0m |
| [0m[1mmRecall [0m [0m[1m 67.26%[0m |
| [0m[1mKappa [0m [0m[1m 43.53%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1324 lr=3.95e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1427 lr=3.92e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1427 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 035 / 100 | Elapsed: 0:11:32[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1427 |
| Val Loss 0.2967 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 47.68%[0m |
| [92m[1mmIoU [0m [92m[1m 33.50%[0m |
| [92m[1mmFscore [0m [92m[1m 46.48%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.77%[0m |
| [0m[1mmRecall [0m [0m[1m 65.93%[0m |
| [0m[1mKappa [0m [0m[1m 41.50%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1374 lr=3.88e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1340 lr=3.85e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1340 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 036 / 100 | Elapsed: 0:11:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1340 |
| Val Loss 0.2928 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 45.73%[0m |
| [92m[1mmIoU [0m [92m[1m 33.63%[0m |
| [92m[1mmFscore [0m [92m[1m 46.41%[0m |
| [0m[1mmPrecision [0m [0m[1m 44.71%[0m |
| [0m[1mmRecall [0m [0m[1m 65.91%[0m |
| [0m[1mKappa [0m [0m[1m 39.95%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1247 lr=3.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1281 lr=3.78e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1281 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 037 / 100 | Elapsed: 0:12:10[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1281 |
| Val Loss 0.3084 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.10%[0m |
| [92m[1mmIoU [0m [92m[1m 36.03%[0m |
| [92m[1mmFscore [0m [92m[1m 49.48%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 45.33%[0m |
| [0m[1mmRecall [0m [0m[1m 69.51%[0m |
| [0m[1mKappa [0m [0m[1m 46.44%[0m |
| ────────────────── ────────── |
| Best mFscore 48.05% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 49.48% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1208 lr=3.74e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1277 lr=3.71e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1277 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 038 / 100 | Elapsed: 0:12:30[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1277 |
| Val Loss 0.2852 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 52.65%[0m |
| [92m[1mmIoU [0m [92m[1m 36.63%[0m |
| [92m[1mmFscore [0m [92m[1m 49.85%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 46.93%[0m |
| [0m[1mmRecall [0m [0m[1m 68.14%[0m |
| [0m[1mKappa [0m [0m[1m 45.89%[0m |
| ────────────────── ────────── |
| Best mFscore 49.48% |
| Val Time 4.1s |
|
|
| [92m[1m★ New best mFscore: 49.85% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1198 lr=3.67e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1192 lr=3.63e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1192 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 039 / 100 | Elapsed: 0:12:50[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1192 |
| Val Loss 0.2981 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 54.74%[0m |
| [92m[1mmIoU [0m [92m[1m 38.00%[0m |
| [92m[1mmFscore [0m [92m[1m 51.34%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 47.32%[0m |
| [0m[1mmRecall [0m [0m[1m 69.56%[0m |
| [0m[1mKappa [0m [0m[1m 47.90%[0m |
| ────────────────── ────────── |
| Best mFscore 49.85% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 51.34% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1095 lr=3.59e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1099 lr=3.56e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1099 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 040 / 100 | Elapsed: 0:13:09[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1099 |
| Val Loss 0.2982 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 48.99%[0m |
| [92m[1mmIoU [0m [92m[1m 35.73%[0m |
| [92m[1mmFscore [0m [92m[1m 48.79%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.19%[0m |
| [0m[1mmRecall [0m [0m[1m 67.73%[0m |
| [0m[1mKappa [0m [0m[1m 42.75%[0m |
| ────────────────── ────────── |
| Best mFscore 51.34% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1118 lr=3.52e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1122 lr=3.49e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1122 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 041 / 100 | Elapsed: 0:13:29[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1122 |
| Val Loss 0.3286 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.59%[0m |
| [92m[1mmIoU [0m [92m[1m 36.47%[0m |
| [92m[1mmFscore [0m [92m[1m 49.57%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.67%[0m |
| [0m[1mmRecall [0m [0m[1m 68.37%[0m |
| [0m[1mKappa [0m [0m[1m 46.99%[0m |
| ────────────────── ────────── |
| Best mFscore 51.34% |
| Val Time 4.1s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1100 lr=3.44e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1088 lr=3.41e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1088 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 042 / 100 | Elapsed: 0:13:48[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1088 |
| Val Loss 0.3311 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 55.68%[0m |
| [92m[1mmIoU [0m [92m[1m 36.90%[0m |
| [92m[1mmFscore [0m [92m[1m 50.24%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.96%[0m |
| [0m[1mmRecall [0m [0m[1m 68.33%[0m |
| [0m[1mKappa [0m [0m[1m 48.78%[0m |
| ────────────────── ────────── |
| Best mFscore 51.34% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1056 lr=3.37e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1055 lr=3.33e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1055 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 043 / 100 | Elapsed: 0:14:06[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1055 |
| Val Loss 0.3263 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.47%[0m |
| [92m[1mmIoU [0m [92m[1m 38.89%[0m |
| [92m[1mmFscore [0m [92m[1m 52.07%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 47.84%[0m |
| [0m[1mmRecall [0m [0m[1m 68.59%[0m |
| [0m[1mKappa [0m [0m[1m 50.55%[0m |
| ────────────────── ────────── |
| Best mFscore 51.34% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 52.07% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1091 lr=3.29e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1056 lr=3.26e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1056 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 044 / 100 | Elapsed: 0:14:26[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1056 |
| Val Loss 0.3238 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 55.58%[0m |
| [92m[1mmIoU [0m [92m[1m 38.32%[0m |
| [92m[1mmFscore [0m [92m[1m 51.67%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.40%[0m |
| [0m[1mmRecall [0m [0m[1m 69.49%[0m |
| [0m[1mKappa [0m [0m[1m 48.85%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1035 lr=3.21e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1063 lr=3.18e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1063 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 045 / 100 | Elapsed: 0:14:45[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1063 |
| Val Loss 0.3168 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 54.10%[0m |
| [92m[1mmIoU [0m [92m[1m 37.16%[0m |
| [92m[1mmFscore [0m [92m[1m 50.45%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.16%[0m |
| [0m[1mmRecall [0m [0m[1m 69.53%[0m |
| [0m[1mKappa [0m [0m[1m 47.60%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1014 lr=3.13e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1069 lr=3.10e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1069 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 046 / 100 | Elapsed: 0:15:04[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1069 |
| Val Loss 0.3314 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 53.20%[0m |
| [92m[1mmIoU [0m [92m[1m 34.59%[0m |
| [92m[1mmFscore [0m [92m[1m 48.00%[0m |
| [0m[1mmPrecision [0m [0m[1m 43.54%[0m |
| [0m[1mmRecall [0m [0m[1m 67.69%[0m |
| [0m[1mKappa [0m [0m[1m 46.51%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.1043 lr=3.05e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.1000 lr=3.02e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.1000 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 047 / 100 | Elapsed: 0:15:23[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.1000 |
| Val Loss 0.3197 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 56.09%[0m |
| [92m[1mmIoU [0m [92m[1m 37.19%[0m |
| [92m[1mmFscore [0m [92m[1m 50.70%[0m |
| [0m[1mmPrecision [0m [0m[1m 45.98%[0m |
| [0m[1mmRecall [0m [0m[1m 69.39%[0m |
| [0m[1mKappa [0m [0m[1m 49.39%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0951 lr=2.97e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0965 lr=2.94e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0965 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 048 / 100 | Elapsed: 0:15:42[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0965 |
| Val Loss 0.3508 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 56.40%[0m |
| [92m[1mmIoU [0m [92m[1m 38.40%[0m |
| [92m[1mmFscore [0m [92m[1m 51.62%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.30%[0m |
| [0m[1mmRecall [0m [0m[1m 69.21%[0m |
| [0m[1mKappa [0m [0m[1m 49.56%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0963 lr=2.89e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0939 lr=2.86e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0939 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 049 / 100 | Elapsed: 0:16:01[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0939 |
| Val Loss 0.3496 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 56.86%[0m |
| [92m[1mmIoU [0m [92m[1m 38.35%[0m |
| [92m[1mmFscore [0m [92m[1m 52.14%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 46.94%[0m |
| [0m[1mmRecall [0m [0m[1m 69.55%[0m |
| [0m[1mKappa [0m [0m[1m 50.23%[0m |
| ────────────────── ────────── |
| Best mFscore 52.07% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 52.14% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0873 lr=2.81e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0890 lr=2.78e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0890 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 050 / 100 | Elapsed: 0:16:21[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0890 |
| Val Loss 0.3435 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.59%[0m |
| [92m[1mmIoU [0m [92m[1m 38.33%[0m |
| [92m[1mmFscore [0m [92m[1m 51.85%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.25%[0m |
| [0m[1mmRecall [0m [0m[1m 69.62%[0m |
| [0m[1mKappa [0m [0m[1m 50.74%[0m |
| ────────────────── ────────── |
| Best mFscore 52.14% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0832 lr=2.73e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0846 lr=2.69e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0846 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 051 / 100 | Elapsed: 0:16:40[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0846 |
| Val Loss 0.3330 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 56.30%[0m |
| [92m[1mmIoU [0m [92m[1m 37.46%[0m |
| [92m[1mmFscore [0m [92m[1m 50.74%[0m |
| [0m[1mmPrecision [0m [0m[1m 46.27%[0m |
| [0m[1mmRecall [0m [0m[1m 69.06%[0m |
| [0m[1mKappa [0m [0m[1m 49.59%[0m |
| ────────────────── ────────── |
| Best mFscore 52.14% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0887 lr=2.65e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0861 lr=2.61e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0861 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 052 / 100 | Elapsed: 0:16:59[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0861 |
| Val Loss 0.3447 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.98%[0m |
| [92m[1mmIoU [0m [92m[1m 39.05%[0m |
| [92m[1mmFscore [0m [92m[1m 52.57%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 47.81%[0m |
| [0m[1mmRecall [0m [0m[1m 69.78%[0m |
| [0m[1mKappa [0m [0m[1m 51.15%[0m |
| ────────────────── ────────── |
| Best mFscore 52.14% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 52.57% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0757 lr=2.57e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0792 lr=2.53e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0792 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 053 / 100 | Elapsed: 0:17:19[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0792 |
| Val Loss 0.3553 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 57.78%[0m |
| [92m[1mmIoU [0m [92m[1m 38.73%[0m |
| [92m[1mmFscore [0m [92m[1m 52.22%[0m |
| [0m[1mmPrecision [0m [0m[1m 47.38%[0m |
| [0m[1mmRecall [0m [0m[1m 69.36%[0m |
| [0m[1mKappa [0m [0m[1m 50.94%[0m |
| ────────────────── ────────── |
| Best mFscore 52.57% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0817 lr=2.49e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0856 lr=2.45e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0856 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 054 / 100 | Elapsed: 0:17:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0856 |
| Val Loss 0.3662 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.01%[0m |
| [92m[1mmIoU [0m [92m[1m 40.37%[0m |
| [92m[1mmFscore [0m [92m[1m 54.00%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 48.66%[0m |
| [0m[1mmRecall [0m [0m[1m 69.80%[0m |
| [0m[1mKappa [0m [0m[1m 54.00%[0m |
| ────────────────── ────────── |
| Best mFscore 52.57% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 54.00% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0842 lr=2.41e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0833 lr=2.37e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0833 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 055 / 100 | Elapsed: 0:17:58[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0833 |
| Val Loss 0.3631 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.07%[0m |
| [92m[1mmIoU [0m [92m[1m 39.92%[0m |
| [92m[1mmFscore [0m [92m[1m 53.37%[0m |
| [0m[1mmPrecision [0m [0m[1m 48.43%[0m |
| [0m[1mmRecall [0m [0m[1m 70.32%[0m |
| [0m[1mKappa [0m [0m[1m 53.16%[0m |
| ────────────────── ────────── |
| Best mFscore 54.00% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0789 lr=2.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0790 lr=2.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0790 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 056 / 100 | Elapsed: 0:18:17[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0790 |
| Val Loss 0.3420 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.64%[0m |
| [92m[1mmIoU [0m [92m[1m 41.29%[0m |
| [92m[1mmFscore [0m [92m[1m 54.80%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.00%[0m |
| [0m[1mmRecall [0m [0m[1m 70.39%[0m |
| [0m[1mKappa [0m [0m[1m 53.77%[0m |
| ────────────────── ────────── |
| Best mFscore 54.00% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 54.80% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0743 lr=2.24e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0752 lr=2.21e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0752 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 057 / 100 | Elapsed: 0:18:37[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0752 |
| Val Loss 0.3716 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.67%[0m |
| [92m[1mmIoU [0m [92m[1m 41.17%[0m |
| [92m[1mmFscore [0m [92m[1m 54.75%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.58%[0m |
| [0m[1mmRecall [0m [0m[1m 70.09%[0m |
| [0m[1mKappa [0m [0m[1m 54.76%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0746 lr=2.16e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0766 lr=2.13e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0766 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 058 / 100 | Elapsed: 0:18:56[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0766 |
| Val Loss 0.3480 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.40%[0m |
| [92m[1mmIoU [0m [92m[1m 40.13%[0m |
| [92m[1mmFscore [0m [92m[1m 53.86%[0m |
| [0m[1mmPrecision [0m [0m[1m 48.64%[0m |
| [0m[1mmRecall [0m [0m[1m 70.04%[0m |
| [0m[1mKappa [0m [0m[1m 53.43%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0737 lr=2.08e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0739 lr=2.05e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0739 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 059 / 100 | Elapsed: 0:19:15[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0739 |
| Val Loss 0.3741 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.06%[0m |
| [92m[1mmIoU [0m [92m[1m 40.95%[0m |
| [92m[1mmFscore [0m [92m[1m 54.62%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.81%[0m |
| [0m[1mmRecall [0m [0m[1m 70.02%[0m |
| [0m[1mKappa [0m [0m[1m 53.18%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0720 lr=2.00e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0724 lr=1.97e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0724 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 060 / 100 | Elapsed: 0:19:34[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0724 |
| Val Loss 0.3570 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.00%[0m |
| [92m[1mmIoU [0m [92m[1m 40.93%[0m |
| [92m[1mmFscore [0m [92m[1m 54.55%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.34%[0m |
| [0m[1mmRecall [0m [0m[1m 70.54%[0m |
| [0m[1mKappa [0m [0m[1m 54.09%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 4.1s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0687 lr=1.92e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0695 lr=1.89e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0695 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 061 / 100 | Elapsed: 0:19:53[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0695 |
| Val Loss 0.3765 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 61.82%[0m |
| [92m[1mmIoU [0m [92m[1m 40.74%[0m |
| [92m[1mmFscore [0m [92m[1m 54.55%[0m |
| [0m[1mmPrecision [0m [0m[1m 48.60%[0m |
| [0m[1mmRecall [0m [0m[1m 70.63%[0m |
| [0m[1mKappa [0m [0m[1m 55.00%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0641 lr=1.84e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0676 lr=1.81e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0676 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 062 / 100 | Elapsed: 0:20:12[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0676 |
| Val Loss 0.3716 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 60.99%[0m |
| [92m[1mmIoU [0m [92m[1m 40.76%[0m |
| [92m[1mmFscore [0m [92m[1m 54.33%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.23%[0m |
| [0m[1mmRecall [0m [0m[1m 70.27%[0m |
| [0m[1mKappa [0m [0m[1m 54.04%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0654 lr=1.77e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0662 lr=1.73e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0662 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 063 / 100 | Elapsed: 0:20:31[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0662 |
| Val Loss 0.3840 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.07%[0m |
| [92m[1mmIoU [0m [92m[1m 41.27%[0m |
| [92m[1mmFscore [0m [92m[1m 54.93%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 49.44%[0m |
| [0m[1mmRecall [0m [0m[1m 70.47%[0m |
| [0m[1mKappa [0m [0m[1m 56.00%[0m |
| ────────────────── ────────── |
| Best mFscore 54.80% |
| Val Time 4.1s |
|
|
| [92m[1m★ New best mFscore: 54.93% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0644 lr=1.69e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0669 lr=1.66e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0669 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 064 / 100 | Elapsed: 0:20:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0669 |
| Val Loss 0.3717 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 62.05%[0m |
| [92m[1mmIoU [0m [92m[1m 40.84%[0m |
| [92m[1mmFscore [0m [92m[1m 54.51%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.15%[0m |
| [0m[1mmRecall [0m [0m[1m 70.33%[0m |
| [0m[1mKappa [0m [0m[1m 55.07%[0m |
| ────────────────── ────────── |
| Best mFscore 54.93% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0652 lr=1.61e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0637 lr=1.58e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0637 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 065 / 100 | Elapsed: 0:21:10[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0637 |
| Val Loss 0.4063 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.06%[0m |
| [92m[1mmIoU [0m [92m[1m 42.34%[0m |
| [92m[1mmFscore [0m [92m[1m 56.06%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.67%[0m |
| [0m[1mmRecall [0m [0m[1m 70.26%[0m |
| [0m[1mKappa [0m [0m[1m 57.08%[0m |
| ────────────────── ────────── |
| Best mFscore 54.93% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 56.06% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0627 lr=1.54e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0635 lr=1.51e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0635 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 066 / 100 | Elapsed: 0:21:30[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0635 |
| Val Loss 0.3908 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 62.52%[0m |
| [92m[1mmIoU [0m [92m[1m 41.41%[0m |
| [92m[1mmFscore [0m [92m[1m 55.17%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.59%[0m |
| [0m[1mmRecall [0m [0m[1m 70.56%[0m |
| [0m[1mKappa [0m [0m[1m 55.63%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0644 lr=1.47e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0648 lr=1.43e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0648 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 067 / 100 | Elapsed: 0:21:49[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0648 |
| Val Loss 0.3899 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.88%[0m |
| [92m[1mmIoU [0m [92m[1m 41.84%[0m |
| [92m[1mmFscore [0m [92m[1m 55.59%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.05%[0m |
| [0m[1mmRecall [0m [0m[1m 70.12%[0m |
| [0m[1mKappa [0m [0m[1m 56.87%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0610 lr=1.39e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0607 lr=1.36e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0607 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 068 / 100 | Elapsed: 0:22:09[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0607 |
| Val Loss 0.3821 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.15%[0m |
| [92m[1mmIoU [0m [92m[1m 41.99%[0m |
| [92m[1mmFscore [0m [92m[1m 55.78%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.25%[0m |
| [0m[1mmRecall [0m [0m[1m 70.55%[0m |
| [0m[1mKappa [0m [0m[1m 56.19%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0582 lr=1.32e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0599 lr=1.29e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0599 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 069 / 100 | Elapsed: 0:22:28[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0599 |
| Val Loss 0.3924 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.67%[0m |
| [92m[1mmIoU [0m [92m[1m 42.23%[0m |
| [92m[1mmFscore [0m [92m[1m 55.91%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.73%[0m |
| [0m[1mmRecall [0m [0m[1m 70.37%[0m |
| [0m[1mKappa [0m [0m[1m 56.72%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0590 lr=1.25e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0595 lr=1.22e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0595 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 070 / 100 | Elapsed: 0:22:47[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0595 |
| Val Loss 0.3820 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.25%[0m |
| [92m[1mmIoU [0m [92m[1m 42.02%[0m |
| [92m[1mmFscore [0m [92m[1m 55.89%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.31%[0m |
| [0m[1mmRecall [0m [0m[1m 70.78%[0m |
| [0m[1mKappa [0m [0m[1m 56.32%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0570 lr=1.18e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0574 lr=1.15e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0574 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 071 / 100 | Elapsed: 0:23:06[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0574 |
| Val Loss 0.4135 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.68%[0m |
| [92m[1mmIoU [0m [92m[1m 41.82%[0m |
| [92m[1mmFscore [0m [92m[1m 55.55%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.77%[0m |
| [0m[1mmRecall [0m [0m[1m 70.54%[0m |
| [0m[1mKappa [0m [0m[1m 56.74%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0572 lr=1.12e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0568 lr=1.09e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0568 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 072 / 100 | Elapsed: 0:23:25[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0568 |
| Val Loss 0.4002 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.21%[0m |
| [92m[1mmIoU [0m [92m[1m 41.87%[0m |
| [92m[1mmFscore [0m [92m[1m 55.61%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.06%[0m |
| [0m[1mmRecall [0m [0m[1m 70.55%[0m |
| [0m[1mKappa [0m [0m[1m 56.27%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0561 lr=1.05e-05 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0560 lr=1.02e-05 eta=0:00:00 |
| [92mDone: avg_loss=0.0560 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 073 / 100 | Elapsed: 0:23:45[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0560 |
| Val Loss 0.4225 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.27%[0m |
| [92m[1mmIoU [0m [92m[1m 42.54%[0m |
| [92m[1mmFscore [0m [92m[1m 56.30%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 50.76%[0m |
| [0m[1mmRecall [0m [0m[1m 70.31%[0m |
| [0m[1mKappa [0m [0m[1m 57.30%[0m |
| ────────────────── ────────── |
| Best mFscore 56.06% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 56.30% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0548 lr=9.88e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0547 lr=9.61e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0547 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 074 / 100 | Elapsed: 0:24:04[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0547 |
| Val Loss 0.4280 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.54%[0m |
| [92m[1mmIoU [0m [92m[1m 42.80%[0m |
| [92m[1mmFscore [0m [92m[1m 56.60%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.01%[0m |
| [0m[1mmRecall [0m [0m[1m 70.60%[0m |
| [0m[1mKappa [0m [0m[1m 57.57%[0m |
| ────────────────── ────────── |
| Best mFscore 56.30% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 56.60% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0556 lr=9.26e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0551 lr=8.99e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0551 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 075 / 100 | Elapsed: 0:24:24[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0551 |
| Val Loss 0.4030 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 63.60%[0m |
| [92m[1mmIoU [0m [92m[1m 41.67%[0m |
| [92m[1mmFscore [0m [92m[1m 55.25%[0m |
| [0m[1mmPrecision [0m [0m[1m 49.60%[0m |
| [0m[1mmRecall [0m [0m[1m 71.05%[0m |
| [0m[1mKappa [0m [0m[1m 56.57%[0m |
| ────────────────── ────────── |
| Best mFscore 56.60% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0572 lr=8.66e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0549 lr=8.40e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0549 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 076 / 100 | Elapsed: 0:24:43[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0549 |
| Val Loss 0.4312 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.81%[0m |
| [92m[1mmIoU [0m [92m[1m 43.20%[0m |
| [92m[1mmFscore [0m [92m[1m 56.99%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.31%[0m |
| [0m[1mmRecall [0m [0m[1m 70.93%[0m |
| [0m[1mKappa [0m [0m[1m 57.89%[0m |
| ────────────────── ────────── |
| Best mFscore 56.60% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 56.99% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0512 lr=8.08e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0515 lr=7.83e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0515 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 077 / 100 | Elapsed: 0:25:03[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0515 |
| Val Loss 0.4285 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.47%[0m |
| [92m[1mmIoU [0m [92m[1m 42.79%[0m |
| [92m[1mmFscore [0m [92m[1m 56.47%[0m |
| [0m[1mmPrecision [0m [0m[1m 50.96%[0m |
| [0m[1mmRecall [0m [0m[1m 70.50%[0m |
| [0m[1mKappa [0m [0m[1m 57.50%[0m |
| ────────────────── ────────── |
| Best mFscore 56.99% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0504 lr=7.52e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0521 lr=7.27e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0521 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 078 / 100 | Elapsed: 0:25:22[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0521 |
| Val Loss 0.4345 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.69%[0m |
| [92m[1mmIoU [0m [92m[1m 42.94%[0m |
| [92m[1mmFscore [0m [92m[1m 56.73%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.09%[0m |
| [0m[1mmRecall [0m [0m[1m 70.61%[0m |
| [0m[1mKappa [0m [0m[1m 57.75%[0m |
| ────────────────── ────────── |
| Best mFscore 56.99% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0518 lr=6.97e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0511 lr=6.74e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0511 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 079 / 100 | Elapsed: 0:25:41[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0511 |
| Val Loss 0.4260 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.85%[0m |
| [92m[1mmIoU [0m [92m[1m 43.17%[0m |
| [92m[1mmFscore [0m [92m[1m 56.98%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.50%[0m |
| [0m[1mmRecall [0m [0m[1m 70.49%[0m |
| [0m[1mKappa [0m [0m[1m 57.89%[0m |
| ────────────────── ────────── |
| Best mFscore 56.99% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0510 lr=6.45e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0508 lr=6.22e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0508 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 080 / 100 | Elapsed: 0:26:00[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0508 |
| Val Loss 0.4383 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 64.94%[0m |
| [92m[1mmIoU [0m [92m[1m 43.35%[0m |
| [92m[1mmFscore [0m [92m[1m 57.12%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.80%[0m |
| [0m[1mmRecall [0m [0m[1m 70.31%[0m |
| [0m[1mKappa [0m [0m[1m 58.03%[0m |
| ────────────────── ────────── |
| Best mFscore 56.99% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 57.12% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0489 lr=5.95e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0496 lr=5.73e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0496 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 081 / 100 | Elapsed: 0:26:20[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0496 |
| Val Loss 0.4459 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.81%[0m |
| [92m[1mmIoU [0m [92m[1m 43.68%[0m |
| [92m[1mmFscore [0m [92m[1m 57.53%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 51.91%[0m |
| [0m[1mmRecall [0m [0m[1m 70.47%[0m |
| [0m[1mKappa [0m [0m[1m 58.85%[0m |
| ────────────────── ────────── |
| Best mFscore 57.12% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 57.53% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0495 lr=5.47e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0492 lr=5.26e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0492 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 082 / 100 | Elapsed: 0:26:39[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0492 |
| Val Loss 0.4530 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.27%[0m |
| [92m[1mmIoU [0m [92m[1m 43.52%[0m |
| [92m[1mmFscore [0m [92m[1m 57.43%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.56%[0m |
| [0m[1mmRecall [0m [0m[1m 70.70%[0m |
| [0m[1mKappa [0m [0m[1m 59.31%[0m |
| ────────────────── ────────── |
| Best mFscore 57.53% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0465 lr=5.01e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0481 lr=4.81e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0481 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 083 / 100 | Elapsed: 0:26:59[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0481 |
| Val Loss 0.4645 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.59%[0m |
| [92m[1mmIoU [0m [92m[1m 43.46%[0m |
| [92m[1mmFscore [0m [92m[1m 57.26%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.63%[0m |
| [0m[1mmRecall [0m [0m[1m 70.40%[0m |
| [0m[1mKappa [0m [0m[1m 58.60%[0m |
| ────────────────── ────────── |
| Best mFscore 57.53% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0486 lr=4.57e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0473 lr=4.39e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0473 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 084 / 100 | Elapsed: 0:27:18[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0473 |
| Val Loss 0.4596 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.93%[0m |
| [92m[1mmIoU [0m [92m[1m 43.89%[0m |
| [92m[1mmFscore [0m [92m[1m 57.72%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 52.19%[0m |
| [0m[1mmRecall [0m [0m[1m 70.55%[0m |
| [0m[1mKappa [0m [0m[1m 58.93%[0m |
| ────────────────── ────────── |
| Best mFscore 57.53% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 57.72% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0464 lr=4.16e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0470 lr=3.99e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0470 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 085 / 100 | Elapsed: 0:27:38[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0470 |
| Val Loss 0.4683 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.06%[0m |
| [92m[1mmIoU [0m [92m[1m 43.71%[0m |
| [92m[1mmFscore [0m [92m[1m 57.61%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.94%[0m |
| [0m[1mmRecall [0m [0m[1m 70.41%[0m |
| [0m[1mKappa [0m [0m[1m 59.07%[0m |
| ────────────────── ────────── |
| Best mFscore 57.72% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0474 lr=3.77e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0468 lr=3.61e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0468 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 086 / 100 | Elapsed: 0:27:57[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0468 |
| Val Loss 0.4665 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 65.83%[0m |
| [92m[1mmIoU [0m [92m[1m 43.54%[0m |
| [92m[1mmFscore [0m [92m[1m 57.46%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.98%[0m |
| [0m[1mmRecall [0m [0m[1m 70.05%[0m |
| [0m[1mKappa [0m [0m[1m 58.88%[0m |
| ────────────────── ────────── |
| Best mFscore 57.72% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0472 lr=3.41e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0467 lr=3.26e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0467 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 087 / 100 | Elapsed: 0:28:16[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0467 |
| Val Loss 0.4778 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.16%[0m |
| [92m[1mmIoU [0m [92m[1m 43.87%[0m |
| [92m[1mmFscore [0m [92m[1m 57.75%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 52.31%[0m |
| [0m[1mmRecall [0m [0m[1m 70.19%[0m |
| [0m[1mKappa [0m [0m[1m 59.18%[0m |
| ────────────────── ────────── |
| Best mFscore 57.72% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 57.75% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0473 lr=3.07e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0463 lr=2.93e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0463 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 088 / 100 | Elapsed: 0:28:36[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0463 |
| Val Loss 0.4576 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.18%[0m |
| [92m[1mmIoU [0m [92m[1m 43.56%[0m |
| [92m[1mmFscore [0m [92m[1m 57.42%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.81%[0m |
| [0m[1mmRecall [0m [0m[1m 70.32%[0m |
| [0m[1mKappa [0m [0m[1m 59.18%[0m |
| ────────────────── ────────── |
| Best mFscore 57.75% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0456 lr=2.75e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0453 lr=2.62e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0453 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 089 / 100 | Elapsed: 0:28:55[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0453 |
| Val Loss 0.4727 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.35%[0m |
| [92m[1mmIoU [0m [92m[1m 43.79%[0m |
| [92m[1mmFscore [0m [92m[1m 57.65%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.06%[0m |
| [0m[1mmRecall [0m [0m[1m 70.12%[0m |
| [0m[1mKappa [0m [0m[1m 59.37%[0m |
| ────────────────── ────────── |
| Best mFscore 57.75% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0459 lr=2.46e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0457 lr=2.34e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0457 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 090 / 100 | Elapsed: 0:29:14[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0457 |
| Val Loss 0.4760 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.54%[0m |
| [92m[1mmIoU [0m [92m[1m 43.97%[0m |
| [92m[1mmFscore [0m [92m[1m 57.82%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 52.32%[0m |
| [0m[1mmRecall [0m [0m[1m 70.13%[0m |
| [0m[1mKappa [0m [0m[1m 59.56%[0m |
| ────────────────── ────────── |
| Best mFscore 57.75% |
| Val Time 3.9s |
|
|
| [92m[1m★ New best mFscore: 57.82% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0452 lr=2.20e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0448 lr=2.09e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0448 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 091 / 100 | Elapsed: 0:29:34[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0448 |
| Val Loss 0.4912 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.85%[0m |
| [92m[1mmIoU [0m [92m[1m 44.19%[0m |
| [92m[1mmFscore [0m [92m[1m 58.06%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 52.63%[0m |
| [0m[1mmRecall [0m [0m[1m 69.98%[0m |
| [0m[1mKappa [0m [0m[1m 59.87%[0m |
| ────────────────── ────────── |
| Best mFscore 57.82% |
| Val Time 4.1s |
|
|
| [92m[1m★ New best mFscore: 58.06% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0446 lr=1.96e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0450 lr=1.86e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0450 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 092 / 100 | Elapsed: 0:29:53[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0450 |
| Val Loss 0.4653 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.47%[0m |
| [92m[1mmIoU [0m [92m[1m 43.85%[0m |
| [92m[1mmFscore [0m [92m[1m 57.70%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.30%[0m |
| [0m[1mmRecall [0m [0m[1m 70.11%[0m |
| [0m[1mKappa [0m [0m[1m 59.43%[0m |
| ────────────────── ────────── |
| Best mFscore 58.06% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0456 lr=1.75e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0453 lr=1.66e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0453 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 093 / 100 | Elapsed: 0:30:13[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0453 |
| Val Loss 0.4822 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 67.17%[0m |
| [92m[1mmIoU [0m [92m[1m 44.34%[0m |
| [92m[1mmFscore [0m [92m[1m 58.20%[0m ★ |
| [0m[1mmPrecision [0m [0m[1m 52.77%[0m |
| [0m[1mmRecall [0m [0m[1m 70.10%[0m |
| [0m[1mKappa [0m [0m[1m 60.16%[0m |
| ────────────────── ────────── |
| Best mFscore 58.06% |
| Val Time 4.0s |
|
|
| [92m[1m★ New best mFscore: 58.20% → saved best_model.pth[0m |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0453 lr=1.56e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0455 lr=1.49e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0455 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 094 / 100 | Elapsed: 0:30:32[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0455 |
| Val Loss 0.4745 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.16%[0m |
| [92m[1mmIoU [0m [92m[1m 43.61%[0m |
| [92m[1mmFscore [0m [92m[1m 57.49%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.88%[0m |
| [0m[1mmRecall [0m [0m[1m 70.17%[0m |
| [0m[1mKappa [0m [0m[1m 59.18%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0438 lr=1.40e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0449 lr=1.34e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0449 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 095 / 100 | Elapsed: 0:30:51[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0449 |
| Val Loss 0.4727 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.09%[0m |
| [92m[1mmIoU [0m [92m[1m 43.59%[0m |
| [92m[1mmFscore [0m [92m[1m 57.47%[0m |
| [0m[1mmPrecision [0m [0m[1m 51.96%[0m |
| [0m[1mmRecall [0m [0m[1m 70.00%[0m |
| [0m[1mKappa [0m [0m[1m 59.10%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0430 lr=1.27e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0436 lr=1.22e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0436 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 096 / 100 | Elapsed: 0:31:11[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0436 |
| Val Loss 0.4773 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.71%[0m |
| [92m[1mmIoU [0m [92m[1m 43.90%[0m |
| [92m[1mmFscore [0m [92m[1m 57.78%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.28%[0m |
| [0m[1mmRecall [0m [0m[1m 70.05%[0m |
| [0m[1mKappa [0m [0m[1m 59.70%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 3.9s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0438 lr=1.16e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0437 lr=1.12e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0437 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 097 / 100 | Elapsed: 0:31:30[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0437 |
| Val Loss 0.4869 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.75%[0m |
| [92m[1mmIoU [0m [92m[1m 43.90%[0m |
| [92m[1mmFscore [0m [92m[1m 57.80%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.19%[0m |
| [0m[1mmRecall [0m [0m[1m 70.10%[0m |
| [0m[1mKappa [0m [0m[1m 59.77%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 4.1s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0435 lr=1.08e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0436 lr=1.05e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0436 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 098 / 100 | Elapsed: 0:31:49[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0436 |
| Val Loss 0.4745 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.92%[0m |
| [92m[1mmIoU [0m [92m[1m 44.18%[0m |
| [92m[1mmFscore [0m [92m[1m 58.00%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.66%[0m |
| [0m[1mmRecall [0m [0m[1m 70.02%[0m |
| [0m[1mKappa [0m [0m[1m 59.91%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0434 lr=1.03e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0438 lr=1.01e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0438 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 099 / 100 | Elapsed: 0:32:08[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0438 |
| Val Loss 0.4930 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.82%[0m |
| [92m[1mmIoU [0m [92m[1m 44.22%[0m |
| [92m[1mmFscore [0m [92m[1m 58.12%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.65%[0m |
| [0m[1mmRecall [0m [0m[1m 70.08%[0m |
| [0m[1mKappa [0m [0m[1m 59.85%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 4.0s |
|
|
| [1mTraining[0m |
| [██████████████████████░░░░░░░░░░░░░░░░░░] 55.6% iter 50/90 loss=0.0435 lr=1.00e-06 eta=0:00:06 |
| [████████████████████████████████████████] 100.0% iter 90/90 loss=0.0438 lr=1.00e-06 eta=0:00:00 |
| [92mDone: avg_loss=0.0438 time=0:00:14[0m |
|
|
| [1mValidating...[0m |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Epoch 100 / 100 | Elapsed: 0:32:27[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| Train Loss 0.0438 |
| Val Loss 0.4836 |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 66.57%[0m |
| [92m[1mmIoU [0m [92m[1m 44.01%[0m |
| [92m[1mmFscore [0m [92m[1m 57.91%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.36%[0m |
| [0m[1mmRecall [0m [0m[1m 70.29%[0m |
| [0m[1mKappa [0m [0m[1m 59.60%[0m |
| ────────────────── ────────── |
| Best mFscore 58.20% |
| Val Time 4.0s |
|
|
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
| [1m[96m Final Test Evaluation[0m |
| [1m[96m══════════════════════════════════════════════════════════════════════[0m |
|
|
| [1mTestidating...[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 Final Test Results[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Metric Value |
| ────────────────── ────────── |
| [0m[1mOA [0m [0m[1m 67.10%[0m |
| [92m[1mmIoU [0m [92m[1m 44.35%[0m |
| [92m[1mmFscore [0m [92m[1m 58.37%[0m |
| [0m[1mmPrecision [0m [0m[1m 52.78%[0m |
| [0m[1mmRecall [0m [0m[1m 70.72%[0m |
| [0m[1mKappa [0m [0m[1m 60.02%[0m |
| ────────────────── ────────── |
| Test Loss 0.5955 |
| Total Time 0:32:37 |
|
|
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
| [1m[93m Per-Class IoU (Test)[0m |
| [1m[93m──────────────────────────────────────────────────────────────────────[0m |
|
|
| Class IoU Bar |
| ────────────────────────────── ─────── ──────────────────── |
| [92mBackground 48.39% █████████[0m |
| [92mMeadow 56.56% ███████████[0m |
| [92mSoft winter wheat 72.22% ██████████████[0m |
| [92mCorn 76.76% ███████████████[0m |
| [92mWinter barley 56.56% ███████████[0m |
| [92mWinter rapeseed 74.17% ██████████████[0m |
| [92mSpring barley 30.51% ██████[0m |
| [92mSunflower 53.94% ██████████[0m |
| [92mGrapevine 36.16% ███████[0m |
| [92mBeet 73.32% ██████████████[0m |
| [92mWinter triticale 25.01% █████[0m |
| [92mWinter durum wheat 43.30% ████████[0m |
| [92mFruits vegetables flowers 22.27% ████[0m |
| [92mPotatoes 37.33% ███████[0m |
| [92mLeguminous fodder 23.17% ████[0m |
| [92mSoybeans 66.81% █████████████[0m |
| [93mOrchard 15.25% ███[0m |
| [93mMixed cereal 17.63% ███[0m |
| [93mSorghum 13.29% ██[0m |
|
|
| [92m[1mAll results saved to: ./work_dirs/fold1_v3[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 |
|
|
