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geonusaf-s4-segnext-t-R0-block-fold1
sugam24/geonusaf-s4-segnext-t-R0-block-fold1
geonusaf-s4-segnext-t-R0-block-fold1 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. The card lists the license as apache-2.0.
Kathmandu Valley land-use segmentation, 6 classes, ignoreindex=255.
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Updated Sep 1, 2026
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026
- SHA
- d1849ef5
Model card
From the Hugging Face model README
GeoNUSAF Stage 4 - SegNeXt-T - arm R0 - block fold 1
Kathmandu Valley land-use segmentation, 6 classes, ignore_index=255.
| field | value |
|---|---|
| architecture | MSCAN-T encoder + LightHamHead decoder (SegNeXt, NeurIPS 2022) |
| arch_sig | c615877e4f3e |
| 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, rand_init=True |
| arm | R0 (real only) |
| train pairs | 804 (804 real + 0 synthetic) |
| synthetic source | sugam24/geonusaf-stage3-fakepairs-block-fold1, fake_pairs_R1 |
| split | block fold 1, train_sha1 eb0aebdd9919 |
| schedule | 6000 steps, warmup 500, cosine 6000 |
| lr (head/encoder) | 0.0006 / 6e-05 |
| class weights | from real |
| seed | 42 |
| best step | 5800 |
| val mIoU | 0.5684 |
| val mF1 | 0.7070 |
| val OA | 0.8366 |
| val kappa | 0.7046 |
Per-class (validation, 136 real fold-1 tiles)
| class | IoU | F1 |
|---|---|---|
| Residential | 0.8576 | 0.9234 |
| Road | 0.4546 | 0.6250 |
| River | 0.4594 | 0.6296 |
| Forest | 0.7461 | 0.8546 |
| UnusedLand | 0.3085 | 0.4715 |
| Agricultural | 0.5845 | 0.7378 |
Validation contains no synthetic pixels in either arm.
best.pt holds model_state (EMA weights when EMA is on), arch, arch_sig, 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).
Not comparable to the part-1 fold-1 SegNeXt run: that one had frozen augmentation (the persistent_workers bug), determinism on, MIN_DELTA=0.0, and an epoch-based schedule.
- License
- apache-2.0
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026