model card: correct weight paths

README.md

@@ -31,8 +31,8 @@ The polar sun circles the horizon, so a shadow cast from thirty directions is a
 
 | path | contents |
 |---|---|
-| `weights/<site>/<run>/net.pt` | UNet state dict, PyTorch. 198 checkpoints: 7 to 8 seeds per 6 km site (`unet1` …), 3 per 16 km site, one `long` run, 20 MB each |
-| `weights/<site>/<run>/params.json` | the per-sun-bin exposure and floor fitted with that network |
+| `weights/<site>/unet/<run>/net.pt` | UNet state dict, PyTorch. 198 checkpoints: 7 to 8 seeds per 6 km site (`unet1` …), 3 per 16 km site, one `long` run, 20 MB each |
+| `weights/<site>/unet/<run>/params.json` | the per-sun-bin exposure and floor fitted with that network |
 | `weights/<site>/split.json` | the frozen train/test split of sun bins for that site |
 | `weights/<site>/sun_fit.csv` | sun direction per bin, nominal and refined |
 | `results/<site>.csv` | held-out scores for every map at that site, plain column names |
@@ -67,7 +67,7 @@ Reproduce a site's map from the public inputs with the pipeline in the code repo
 export MOONDEM_DATA=data/sites/haworth MOONDEM_BOX=haworth
 python pipeline/step1_cut_photos.py && python pipeline/step2_get_laser_heights.py && python pipeline/step3_check_sun_angles.py
 cp weights/haworth/split.json $MOONDEM_DATA/
-python pipeline/step5_fit_terrain_network.py --infer-only weights/haworth/unet1/net.pt --tag from_checkpoint
+python pipeline/step5_fit_terrain_network.py --infer-only weights/haworth/unet/unet1/net.pt --tag from_checkpoint
 ```
 
 The architecture is a five-level UNet (widths 32, 64, 128, 256, 256) that takes 2·N+1 channels (N training bins, their valid masks, the LOLA base) and outputs a height residual scaled by 5 m. Input stacks, LOLA windows and the solver are in the code repository.