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sugam24/geonusaf-unet-r18-block-fold0
geonusaf-unet-r18-block-fold0 is a machine learning model from sugam24. 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 segmentation-models-pytorch.
Kathmandu Valley land-use segmentation, 6 classes, ignoreindex=255. Weights are the EMA weights (decay 0.999), not the raw final weights.
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Updated Aug 22, 2026
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.pt250 MB · 99%
From the Hugging Face model README
Kathmandu Valley land-use segmentation, 6 classes, ignore_index=255. Weights are the EMA weights (decay 0.999), not the raw final weights.
| field | value |
|---|---|
| architecture | smp.Unet, encoder resnet18 (ImageNet), decoder [128, 64, 32, 16, 8] |
| params | 12.46 M |
| arch version | unet-r18-v1 |
| split mode | block |
| fold | 0 of 3 |
| seed | 42 |
| input | 512x512, ImageNet norm, effective GSD 0.586 m/px |
| regularization | wd 0.01 (norm/bias exempt), ls 0.05, drop 0.1, EMA 0.999 |
| classes | Residential, Road, River, Forest, UnusedLand, Agricultural |
| best epoch | 68 |
| val mIoU | 0.3213 |
| val mF1 | 0.4444 |
| val OA | 0.6051 |
| val kappa | 0.4785 |
| class | IoU | F1 |
|---|---|---|
| Residential | 0.5658 | 0.7227 |
| Road | 0.1600 | 0.2758 |
| River | 0.0070 | 0.0140 |
| Forest | 0.5939 | 0.7452 |
| UnusedLand | 0.2065 | 0.3423 |
| Agricultural | 0.3948 | 0.5661 |
Checkpoint best.pt holds model_state (EMA) plus cfg, metrics and arch_sig.