---
pipeline_tag: image-segmentation
library_name: transformers
base_model: gdurkin/cdl_mask2former_v3_mspc
license: apache-2.0
tags:
- mask2former
- semantic-segmentation
- image-segmentation
- remote-sensing
- aerial-imagery
- wildfire-risk
datasets:
- gdurkin/fire_risk_properties
metrics:
- mean_iou
- fw_iou
model-index:
- name: cali_fire_risk
  results:
  - task:
      type: image-segmentation
      name: Image Segmentation
    dataset:
      name: Fire Risk Properties (NAIP 512px)
      type: gdurkin/fire_risk_properties
    metrics:
    - type: mean_iou
      value: 0.460028
    - type: fw_iou
      value: 0.580636
---

# 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]