Hugging Face's logo

Dhruv1000
/
Swin-PASTIS

PyTorch
English
remote-sensing
semantic-segmentation
agriculture
crop-mapping
sentinel-2
swin-transformer
stcln
pastis
amd-rocm
Eval Results (legacy)
Model card Files
xet
 Community
Swin-PASTIS / work_dirs /fold1_v2.log
Dhruv1000's picture
Dhruv1000
Duplicate from Dhruv1000/AgriFM_PASTIS
05a59a6
Raw
History Blame Contribute Delete
13.9 kB
/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]
══════════════════════════════════════════════════════════════════════
 AgriFM × PASTIS — Fold 1 | 2026-04-17 03:23:59
══════════════════════════════════════════════════════════════════════
Device :
VRAM : 191.7 GB
 AMP : True
 Batch size : 16
 Epochs : 100
 LR : 3e-05
 Weight decay : 0.05
 Warmup iters : 500
 Augmentation : True (flip+rotate)
 Class weights : True
 Work dir : ./work_dirs/fold1_v2
──────────────────────────────────────────────────────────────────────
 Building Datasets
──────────────────────────────────────────────────────────────────────
PASTIS fold=1 split=train: 1279 patches (augment=True)
PASTIS fold=1 split=val: 623 patches (augment=False)
PASTIS fold=1 split=test: 531 patches (augment=False)
Train: 1279 Val: 623 Test: 531
 Train batches: 79 Val batches: 39
──────────────────────────────────────────────────────────────────────
 Building Model
──────────────────────────────────────────────────────────────────────
Parameters: 196.2M
──────────────────────────────────────────────────────────────────────
 Computing Class Weights
──────────────────────────────────────────────────────────────────────
Class weights (fold=1, sampled 300 patches):
 Class Count Weight
 ────────────────────────────── ────────── ────────
 Background 1978879 0.010
 Meadow 1072189 0.018
 Soft winter wheat 356296 0.053
 Corn 524472 0.036
 Winter barley 98052 0.193
 Winter rapeseed 68225 0.277
 Spring barley 28348 0.666
 Sunflower 39655 0.476
 Grapevine 69535 0.272
 Beet 33292 0.567
 Winter triticale 51347 0.368
 Winter durum wheat 2307 8.188
 Fruits vegetables flowers 18938 0.998
 Potatoes 8376 2.255
 Leguminous fodder 47354 0.399
 Soybeans 88388 0.214
 Orchard 8118 2.327
 Mixed cereal 32384 0.583
 Sorghum 17172 1.100
══════════════════════════════════════════════════════════════════════
 Training | 100 epochs | lr=3e-05 wd=0.05 fold=1
══════════════════════════════════════════════════════════════════════
Training
 [█████████████████████████░░░░░░░░░░░░░░░] 63.3% iter 50/79 loss=0.4094 lr=3.90e-06 eta=0:00:12
 [████████████████████████████████████████] 100.0% iter 79/79 loss=0.3903 lr=5.58e-06 eta=0:00:00
 Done: avg_loss=0.3903 time=0:00:32
Validating...
──────────────────────────────────────────────────────────────────────
 Epoch 001 / 100 | Elapsed: 0:00:42
──────────────────────────────────────────────────────────────────────
Metric Value
 ────────────────── ──────────
 Train Loss 0.3903
 Val Loss 3.5759
 ────────────────── ──────────
 OA   4.75%
 mIoU   1.06%
 mFscore   1.91% ★
 mPrecision   2.96%
 mRecall   7.93%
 Kappa   2.28%
 ────────────────── ──────────
 Best mFscore 0.00%
 Val Time 8.4s
★ New best mFscore: 1.91% → saved best_model.pth
Training
 [█████████████████████████░░░░░░░░░░░░░░░] 63.3% iter 50/79 loss=0.2860 lr=8.48e-06 eta=0:00:10
 [████████████████████████████████████████] 100.0% iter 79/79 loss=0.2736 lr=1.02e-05 eta=0:00:00
 Done: avg_loss=0.2736 time=0:00:27
Validating...
──────────────────────────────────────────────────────────────────────
 Epoch 002 / 100 | Elapsed: 0:01:19
──────────────────────────────────────────────────────────────────────
Metric Value
 ────────────────── ──────────
 Train Loss 0.2736
 Val Loss 3.3183
 ────────────────── ──────────
 OA   1.78%
 mIoU   0.83%
 mFscore   1.60%
 mPrecision   7.91%
 mRecall   6.81%
 Kappa   -0.14%
 ────────────────── ──────────
 Best mFscore 1.91%
 Val Time 6.2s
Training
 [█████████████████████████░░░░░░░░░░░░░░░] 63.3% iter 50/79 loss=0.2220 lr=1.31e-05 eta=0:00:10
 [████████████████████████████████████████] 100.0% iter 79/79 loss=0.2256 lr=1.47e-05 eta=0:00:00
 Done: avg_loss=0.2256 time=0:00:27
Validating...
──────────────────────────────────────────────────────────────────────
 Epoch 003 / 100 | Elapsed: 0:01:55
──────────────────────────────────────────────────────────────────────
Metric Value
 ────────────────── ──────────
 Train Loss 0.2256
 Val Loss 4.0984
 ────────────────── ──────────
 OA   4.30%
 mIoU   0.62%
 mFscore   1.20%
 mPrecision   2.39%
 mRecall   6.68%
 Kappa   0.64%
 ────────────────── ──────────
 Best mFscore 1.91%
 Val Time 6.2s
Training
 [█████████████████████████░░░░░░░░░░░░░░░] 63.3% iter 50/79 loss=0.1965 lr=1.76e-05 eta=0:00:10
 [████████████████████████████████████████] 100.0% iter 79/79 loss=0.2048 lr=1.93e-05 eta=0:00:00
 Done: avg_loss=0.2048 time=0:00:27
Validating...
──────────────────────────────────────────────────────────────────────
 Epoch 004 / 100 | Elapsed: 0:02:31
──────────────────────────────────────────────────────────────────────
Metric Value
 ────────────────── ──────────
 Train Loss 0.2048
 Val Loss 4.9570
 ────────────────── ──────────
 OA   5.15%
 mIoU   0.66%
 mFscore   1.27%
 mPrecision   9.16%
 mRecall   5.90%
 Kappa   0.98%
 ────────────────── ──────────
 Best mFscore 1.91%
 Val Time 6.2s
Training
 [█████████████████████████░░░░░░░░░░░░░░░] 63.3% iter 50/79 loss=0.1945 lr=2.22e-05 eta=0:00:10
 [████████████████████████████████████████] 100.0% iter 79/79 loss=0.1903 lr=2.39e-05 eta=0:00:00
 Done: avg_loss=0.1903 time=0:00:27
Validating...
──────────────────────────────────────────────────────────────────────
 Epoch 005 / 100 | Elapsed: 0:03:07
──────────────────────────────────────────────────────────────────────
Metric Value
 ────────────────── ──────────
 Train Loss 0.1903
 Val Loss 10.4665
 ────────────────── ──────────
 OA   0.73%
 mIoU   0.39%
 mFscore   0.77%
 mPrecision   11.22%
 mRecall   5.50%
 Kappa   0.11%