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| field | value |
|---|---|
| architecture | MSCAN-T encoder + LightHamHead decoder (SegNeXt, NeurIPS 2022) |
| encoder init | ImageNet-1K, 100.0% of tensors loaded |
| params | 4.23 M |
| head fuse stride | 8 (stages [1, 2, 3]) |
| NMF rank / steps | R=16, 6 train / 7 eval |
| split mode | random |
| fold | 0 of 3 |
| seed | 42 |
| input | 512x512, ImageNet norm, effective GSD 0.586 m/px |
| classes | Residential, Road, River, Forest, UnusedLand, Agricultural |
| lr (head/encoder) | 0.0006 / 6e-05 |
| regularization | wd 0.01, drop_path 0.1, smooth 0.05, EMA True |
| best epoch | 155 |
| val mIoU | 0.5646 |
| val mF1 | 0.7113 |
| val OA | 0.8078 |
| val kappa | 0.6897 |
| class | IoU | F1 |
|---|---|---|
| Residential | 0.8325 | 0.9086 |
| Road | 0.4619 | 0.6320 |
| River | 0.5492 | 0.7090 |
| Forest | 0.6079 | 0.7561 |
| UnusedLand | 0.3667 | 0.5366 |
| Agricultural | 0.5696 | 0.7258 |
best.pt holds model_state (EMA weights when EMA is on), arch (the dict needed to rebuild the network), the run cfg, and metrics. Rebuild with segnext_model.py from this same repo. Model code derives from Visual-Attention-Network/SegNeXt (Apache-2.0).