README.md

metadata

license: other
license_name: ANRF Open License
license_link: >-
  https://anrfonline.in/ANRF/AbstractFilePath?FileType=E&FileName=OL_AISE.pdf&PathKey=DOCUMENT_TEMPLATE
tags:
  - segmentation
  - remote-sensing
  - flood
  - pytorch

ANRF AISEHack Phase 2 — ensemble artifacts

Checkpoints (bucket root)

These are the weights behind the five-model majority-vote ensemble (~0.227 public LB).

File	Model
siamese_efficientnet_b4_epoch45.ckpt	Dual-branch Siamese U-Net, EfficientNet-B4
kd_transductive_epoch35.ckpt	Same architecture, knowledge-distilled
siamese_efficientnet_b7_epoch35.ckpt	Siamese U-Net, EfficientNet-B7
baseline_21ch_swa.pth	21-channel U-Net (SWA weights)
convnext_large_3class_best.pth	ConvNeXt-Large 3-class U-Net

Prediction CSVs (ensemble_five_csv/)

Folder ensemble_five_csv/ in this bucket holds the five submission CSVs (RLE masks) for those models, with the preferred filenames (01_… through 05_…). They match the stack used for the public LB submission. Sync with:

hf sync hf://buckets/itikelabhaskar/AISE_PHASE2/ensemble_five_csv ./local_folder

License: ANRF Open License — see LICENSE_ANRF.txt in this repo and the competition PDF.

Code: GitHub — anrf_flood_phase2_submission (notebooks + config/models_manifest.json).

Inference: Training code for re-running the models lives outside this bundle (e.g. phase2_final/experiments/ in the author’s codebase). The release notebooks merge precomputed CSVs; full re-inference needs matching SAR/aux preprocessing.