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geonusaf-segnext-t-block-fold0
Pranilllllll/geonusaf-segnext-t-block-fold0
geonusaf-segnext-t-block-fold0 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. The card lists the license as apache-2.0.
Kathmandu Valley land-use segmentation, 6 classes, ignoreindex=255.
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Public
Updated Aug 22, 2026
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 22, 2026
- Updated
- Aug 22, 2026
- SHA
- a3810b72
Model card
From the Hugging Face model README
GeoNUSAF - SegNeXt-T - block split, fold 0
Kathmandu Valley land-use segmentation, 6 classes, ignore_index=255.
| 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 | block |
| 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 | 178 |
| val mIoU | 0.3348 |
| val mF1 | 0.4616 |
| val OA | 0.6118 |
| val kappa | 0.4861 |
Per-class (validation)
| class | IoU | F1 |
|---|---|---|
| Residential | 0.6210 | 0.7662 |
| Road | 0.2535 | 0.4045 |
| River | 0.0607 | 0.1145 |
| Forest | 0.5553 | 0.7140 |
| UnusedLand | 0.0980 | 0.1784 |
| Agricultural | 0.4201 | 0.5916 |
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).
- License
- apache-2.0
- Created
- Aug 22, 2026
- Updated
- Aug 22, 2026