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geonusaf-unetformer-r18-random-fold2
Pranilllllll/geonusaf-unetformer-r18-random-fold2
geonusaf-unetformer-r18-random-fold2 is a machine learning model from Pranilllllll. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch.
Kathmandu Valley land-use segmentation, 6 classes, ignoreindex=255.
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Updated Aug 23, 2026
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.pt235 MB · 98%
At a glance
- Library
- pytorch
- Access
- Public
- Created
- Aug 23, 2026
- Updated
- Aug 23, 2026
- SHA
- 8e395042
Model card
From the Hugging Face model README
GeoNUSAF - UNetFormer (ResNet-18) - random split, fold 2
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 | 2 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 | 84 |
| val mIoU | 0.4905 |
| val mF1 | 0.6378 |
| val OA | 0.7743 |
| val kappa | 0.6551 |
Per-class (validation)
| class | IoU | F1 |
|---|---|---|
| Residential | 0.8151 | 0.8981 |
| Road | 0.3444 | 0.5124 |
| River | 0.3309 | 0.4972 |
| Forest | 0.6426 | 0.7824 |
| UnusedLand | 0.2989 | 0.4603 |
| Agricultural | 0.5108 | 0.6762 |
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.
- Library
- pytorch
- Created
- Aug 23, 2026
- Updated
- Aug 23, 2026