gdurkin/cali_fire_risk

Fire-risk Superbuckets Mask2Former (fine-tuned)

Base: gdurkin/cdl_mask2former_v3_mspc
Labels: ['background', 'road_paved', 'dirt_gravel', 'grass_dry', 'grass_healthy', 'vegetation', 'water', 'building_all']

This repo hosts a Mask2Former model fine-tuned on NAIP 512×512 chips for wildfire-related landcover “superbuckets.”

• Checkpoint source: gdurkin/cali_fire_risk@best-20250920_160245
• Export time: 2025-09-20 16:40:47Z

Evaluation

• mIoU: 0.4600
• FWIoU (frequency-weighted IoU): 0.5806

FWIoU is the mean IoU weighted by each class's pixel frequency: sum_c f_c * IoU_c. It emphasizes overall pixelwise accuracy while still penalizing mistakes.

Per-class IoU

id	label	IoU	support
0	background	0.0000	172666
1	road_paved	0.6855	80261762
2	dirt_gravel	0.3848	57473062
3	grass_dry	0.2654	22281420
4	grass_healthy	0.4975	40281607
5	vegetation	0.6658	47722088
6	water	0.4366	3090841
7	building_all	0.7445	59095050

Usage

import torch
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation

repo = "gdurkin/cali_fire_risk"
rev  = "best-20250920_160245"  # or a tag like "v0.1"

processor = AutoImageProcessor.from_pretrained(repo, revision=rev)
model = Mask2FormerForUniversalSegmentation.from_pretrained(repo, revision=rev).eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# pv: FloatTensor[B,3,H,W] normalized per `processor`
with torch.no_grad():
    out = model(pixel_values=pv.to(device))
pred = processor.post_process_semantic_segmentation(out, target_sizes=[(H, W)])[0]