5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m

LFM2.5-VL-450M VRSBench + MethaneS2CM Expert

Model Description

This is a fine-tuned version of LiquidAI's LFM2.5-VL-450M vision-language model, specialized for methane plume detection in satellite imagery. The model was trained in two stages:

1. VRSBench Training: Base training on VRSBench dataset
2. MethaneS2CM Fine-tuning: Additional training on MethaneS2CM dataset for methane detection

The model can detect methane plumes in Sentinel-2 satellite images and provide bounding box coordinates for the plume location.

Training Details

Stage 1: VRSBench Pre-training

• Base Model: LFM2.5-VL-450M
• Dataset: VRSBench (Vision Reasoning and Scene Understanding Benchmark)
• Epochs: 1
• Method: LoRA (r=16, alpha=32)

Stage 2: Methane Detection Fine-tuning

• Base Model: VRSBench-trained model (5ch4um1/lfm2.5-vrsbench-lora-450m)
• Dataset: MethaneS2CM (Methane Sentinel-2 Community Model)
  • Source: H1deaki/MethaneS2CM on Hugging Face
  • 257,096 training samples, 60,567 test samples
  • Hand-annotated plume masks for bounding box extraction
  • Sentinel-2 bands: SWIR22 (Band 12) → Red, NIR08 (Band 8) → Green, Red (Band 4) → Blue
• Training Samples: 20,000 (10k positive with plumes, 10k negative)
• Epochs: 2
• Method: LoRA (r=16, alpha=32)
• Hardware: Local GPU training (no Ray/distributed)

Evaluation Results

Methane Detection (500 test samples)

Metric	BASE VRSBench	METHANE EXPERT (this model)	Improvement
Accuracy	49.80%	51.00%	+1.20%
Precision	0.00%	50.79%	+50.79%
Recall	0.00%	88.89%	+88.89%
F1 Score	0.00%	64.65%	+64.65%
Mean IoU (bbox)	0.0000	0.5879	+0.5879

Key Results:

• The base VRSBench model cannot predict methane plumes or bounding boxes (0% recall, 0 IoU)
• The Methane Expert model achieves 88.89% recall - excellent at detecting plumes
• Bounding box predictions have 0.5879 mean IoU (good localization)
• Precision is 50.79% - the model tends to over-predict plumes (217 false positives vs 31 true negatives)
• 113 out of 224 predicted bounding boxes have IoU > 0.5 (good localization)

Usage

With llama.cpp

# Download Q4_K_M quantized version (recommended)
wget https://huggingface.co/5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m/resolve/main/lfm2.5-vrsbench-methane-450m-q4_k_m.gguf

# Run inference
./llama-cli -m lfm2.5-vrsbench-methane-450m-q4_k_m.gguf \
  --image satellite_methane_image.png \
  -p "Is there a methane plume in this satellite image? If yes, provide the bounding box as [x1,y1,x2,y2] normalized to 0-1."

With Transformers

from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image
import json

model = AutoModelForVision2Seq.from_pretrained(
    "5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m",
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m")

image = Image.open("satellite_methane_image.png")
prompt = "Is there a methane plume in this satellite image? If yes, provide the bounding box as [x1,y1,x2,y2] normalized to 0-1."

conversation = [
    {"role": "user", "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": prompt}
    ]}
]

text = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100)
pred_text = processor.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(json.loads(pred_text))

GGUF Quantizations

Version	Size	Description
F16	679 MB	Full precision (16-bit)
Q8_0	362 MB	8-bit quantization
Q4_K_M	219 MB	4-bit quantization (recommended for most use cases)

Training Dataset: MethaneS2CM

The model was trained on the MethaneS2CM (Methane Sentinel-2 Community Model) dataset:

• Dataset: H1deaki/MethaneS2CM
• Paper: "MethaneS2CM: A Dataset for Multispectral Deep Methane Emission Detection" (Liu et al., 2025)
• Data: 257k+ samples from Sentinel-2 imagery (2016-2024)
• Annotations: Hand-annotated plume masks for bounding box extraction
• Bands used: False color composite (SWIR22→R, NIR08→G, Red→B)
• Task: Methane plume detection with bounding box regression

Model Sources

• Base Model: LiquidAI/LFM2.5-VL-450M
• VRSBench Model: 5ch4um1/lfm2.5-vrsbench-lora-450m
• Methane Expert Model: 5ch4um1/lfm2.5-vrsbench-MethaneS2CM-methane-lora-450m
• MethaneS2CM Dataset: H1deaki/MethaneS2CM

Limitations

• Model trained on 32x32 Sentinel-2 patches - may not generalize to other resolutions
• Performance depends on quality of false color composite (SWIR22/NIR08/Red)
• Hand-annotated masks may contain annotation errors
• Best performance on methane plumes similar to training distribution

Training Environment

• Framework: Transformers + PEFT (LoRA) + leap-finetune
• Hardware: Local GPU (CUDA)
• Training Scripts: Available in the cookbook repository