---
tags: [semantic-segmentation, remote-sensing, unetformer, resnet18, geonusaf]
library_name: pytorch
---

# GeoNUSAF - UNetFormer (ResNet-18) - random split, fold 1

Kathmandu Valley land-use segmentation, 6 classes, ignore_index=255.

| field | value |
|---|---|
| architecture | UNetFormer, timm resnet18 encoder (ImageNet), global-local attention decoder |
| arch version | unetformer-r18-v2 (sig 29554ac657c8) |
| split mode | random |
| fold | 1 of 3 |
| seed | 42 |
| input | 512x512, ImageNet norm, effective GSD 0.586 m/px |
| classes | Residential, Road, River, Forest, UnusedLand, Agricultural |
| lr (dec/enc) | 0.0003 / 3e-05, AdamW wd 0.01 |
| schedule | 500-step warmup, cosine over 120 epochs |
| regularization | EMA 0.999, label smoothing 0.05, drop path 0.1, dropout 0.1 |
| aux head weight | 0.4 |
| weights | EMA |
| best epoch | 80 |
| val mIoU | 0.5007 |
| val mF1 | 0.6505 |
| val OA | 0.7755 |
| val kappa | 0.6465 |

## Per-class (validation)
| class | IoU | F1 |
|---|---|---|
| Residential | 0.8208 | 0.9016 |
| Road | 0.3525 | 0.5213 |
| River | 0.3918 | 0.5630 |
| Forest | 0.6285 | 0.7719 |
| UnusedLand | 0.3306 | 0.4969 |
| Agricultural | 0.4797 | 0.6484 |

Split caveat: `block` is sequence-block CV using an export-order proxy, not spatial CV.

LR deviates from the UNetFormer paper's 6e-4 (reduced to 3e-4 for parity with the other baselines at this dataset scale).

Architecture is an independent implementation of Wang et al. (2022), ISPRS J. Photogramm. Remote Sens. 190:196-214 (the reference repo is GPL-3.0).

Checkpoint `best.pt` holds `model_state` (EMA weights) plus `cfg`, `metrics`, `arch_sig`.