---
base_model: LiquidAI/LFM2.5-VL-450M
library_name: peft
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
  - lora
  - peft
  - sft
  - satellite-imagery
  - wildfire-detection
  - sentinel-2
  - remote-sensing
  - liquid-ai
  - hackathon
---

# FireEdge LoRA — Wildfire Detection from Sentinel-2 Imagery

LoRA fine-tune of [LiquidAI/LFM2.5-VL-450M](https://huggingface.co/LiquidAI/LFM2.5-VL-450M) for binary wildfire detection from Sentinel-2 pseudo-color composites.

Developed for the **Liquid AI × DPhi Space "AI in Space" Hackathon** (2026).  
Training code & full pipeline: [github.com/YujiYamaguchi/liquid-ai-space-hackathon](https://github.com/YujiYamaguchi/liquid-ai-space-hackathon) (apps/fireedge/)

---

## Task

Given a Sentinel-2 pseudo-color image (R=SWIR22/B12, G=NIR/B8A, B=SWIR16/B11) of a 5km × 5km scene, predict whether an active wildfire is occurring.

```json
{"fire_detected": true}   // or false
```

---

## Model Details

| Item | Value |
|---|---|
| Base model | LiquidAI/LFM2.5-VL-450M (450M params) |
| Adapter type | LoRA |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, out_proj, in_proj, w1, w2, w3, linear_1, linear_2 |
| Loss masking | Assistant-only (mask_asst) |
| Epochs | 5 |
| Learning rate | 2e-4 (cosine, warmup_ratio=0.05) |
| Batch size (effective) | 8 (per_device=1 × grad_accum=8) |
| Hardware | NVIDIA RTX 5090 (24GB VRAM) |

---

## Dataset

| Split | POS (fire) | NEG (no-fire) | Total |
|---|---|---|---|
| Train | 70 | 140 | 210 |
| Val | 15 | 30 | 45 |
| Test | 15 | 30 | 45 |

- **POS**: Active wildfire scenes sourced from NASA FIRMS VIIRS detections × Sentinel-2 via SimSat API
- **NEG (FIRMS)**: Confirmed fire-free scenes near FIRMS detections (temporal offset)
- **NEG (diverse)**: Geographically diverse non-fire scenes across multiple biomes
- **GT source**: NASA FIRMS VIIRS NRT — real satellite fire detections, no synthetic data
- **Image format**: 3-channel PNG (R=B12/SWIR22, G=B8A/NIR, B=B11/SWIR16), 224×224px

---

## Evaluation Results

Tested on a held-out test set (45 samples, stratified split).

| Metric | Base LFM2.5-VL | FireEdge LoRA | Δ |
|---|---|---|---|
| **Precision** | 0.333 | **1.000** | +0.667 |
| **Recall** | 1.000 | **0.933** | −0.067 |
| **F1** | 0.500 | **0.966** | +0.466 |
| **FP Rate** | 1.000 | **0.000** | −1.000 |
| Accuracy | 0.333 | **0.978** | +0.644 |
| Latency mean (ms) | 163 | **150** | −13 |
| JSON parse rate | 1.000 | 1.000 | — |

**Confusion matrix (FireEdge LoRA):**
```
              Predicted
              NO-FIRE   FIRE
True NO-FIRE    30        0    ← 0 false alarms
True FIRE        1       14   ← 1 miss
```

The base model always predicts FIRE (FP Rate = 1.0). LoRA fine-tuning reduces FP Rate to 0.0 while maintaining Recall ≥ 0.85.

---

## Training Methodology

### Loss Masking Strategy

We compared two SFT loss strategies:

| Strategy | Description | Recall | FP Rate |
|---|---|---|---|
| `mask_asst` (this model) | Loss only on assistant tokens | **0.933** | **0.000** |
| `full_seq` | Loss on all tokens | 0.000 | 0.000 |

`full_seq` collapsed to always predicting `false` because the fixed system/user template tokens (~242 tokens) dominate the sequence, leaving the 10-token assistant response (~2.2% of total) with negligible gradient signal.

### Input Format

```
<|im_start|>system
You are a satellite imagery analyst...
<|im_end|>
<|im_start|>user
[196 image tokens] Analyze this Sentinel-2 image...
<|im_end|>
<|im_start|>assistant
{"fire_detected": true/false}
<|im_end|>
```

---

## Usage

```python
from peft import PeftModel
from transformers import AutoProcessor
from PIL import Image

# Load base model + adapter
base_model_id = "LiquidAI/LFM2.5-VL-450M"
adapter_id = "YujiYamaguchi/lfm2-5-vl-450m-wildfire"

# See training code for full inference pipeline:
# apps/fireedge/fireedge/inference.py
```

Full inference code in the training repository.

---

## Citation

```bibtex
@misc{yamaguchi2026fireedge,
  title  = {FireEdge: Onboard Wildfire Detection via LoRA Fine-tuning of LFM2.5-VL},
  author = {Yuji Yamaguchi},
  year   = {2026},
  note   = {Liquid AI × DPhi Space AI in Space Hackathon}
}
```