WayBob/Qwen3VL-8B-QLora-4bit-xView2-Disaster-Recognition

Qwen3VL-8B QLora 4-bit - xView2 Disaster Recognition

🌍 Disaster Recognition Model | 🚨 Emergency Response | 🗣️ Trilingual (EN/JA/ZH)

Built with Qwen3-VL | Fine-tuning: 4-bit QLoRA | Framework: LLaMA-Factory | Languages: English, Japanese, Chinese

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A multilingual vision-language model fine-tuned from Qwen/Qwen3-VL-8B-Instruct for disaster type recognition using 4-bit QLoRA on the xView2 dataset.

Model Description

This model specializes in identifying disaster types from satellite/aerial imagery. Through LoRA fine-tuning on 55,008 trilingual (English/Japanese/Chinese) disaster images, it learns to accurately classify various disaster types including fires, floods, hurricanes, earthquakes, tsunamis, and volcanic eruptions.

Key Capabilities

• 🔥 Fire/Wildfire Recognition - Identifies fire disasters from aerial imagery
• 🌊 Flood Detection - Recognizes flooding disasters from satellite/aerial images
• 🌀 Hurricane/Wind Damage - Detects wind disasters and hurricane impacts
• 🏚️ Earthquake Damage - Identifies earthquake-affected areas
• 🌋 Volcanic Disasters - Recognizes volcanic disaster patterns
• 🌊 Tsunami Impact - Tsunami disaster identification
• 🗣️ Trilingual Support - Responds accurately in English, Japanese, and Chinese

Quick Start

What is This Model?

This is a LoRA adapter (not a full model). You need to:

1. Load the base model: Qwen/Qwen3-VL-8B-Instruct
2. Apply this LoRA adapter on top of it

Advantage: Only ~22MB adapter download instead of ~8.7GB full model!

Installation

git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e .

Usage

from llamafactory.chat import ChatModel

# Initialize model with LoRA adapter
chat_model = ChatModel(args={
    "model_name_or_path": "Qwen/Qwen3-VL-8B-Instruct",
    "adapter_name_or_path": "WayBob/Qwen3VL-8B-QLora-4bit-xView2-Disaster-Recognition",
    "template": "qwen3_vl_nothink",
    "quantization_bit": 4,
    "trust_remote_code": True,
    "flash_attn": "fa2",  # Optional: enable flash attention for faster inference
    "infer_backend": "huggingface",
})

# Ask about disaster type in image
messages = [{"role": "user", "content": "<image>\nWhat type of disaster occurred in this image?"}]
responses = chat_model.chat(messages=messages, images=["disaster_image.png"])
print(responses[0].response_text)  # Output: "Fire disaster"

# Works in Japanese too
messages_ja = [{"role": "user", "content": "<image>\nこの画像ではどのような種類の災害が発生しましたか？"}]
responses_ja = chat_model.chat(messages=messages_ja, images=["disaster_image.png"])
print(responses_ja[0].response_text)  # Output: "火災災害"

# And Chinese
messages_zh = [{"role": "user", "content": "<image>\n这张图片中发生了什么类型的灾害？"}]
responses_zh = chat_model.chat(messages=messages_zh, images=["disaster_image.png"])
print(responses_zh[0].response_text)  # Output: "火灾"

Hardware Requirements

Configuration	VRAM Required
4-bit Quantization (as used in training)	~10-12GB
Inference only	~8-10GB

Recommended GPU: RTX 3090 / 4090 / A100 or equivalent with 12GB+ VRAM

Training Details

Base Model

• Source: Qwen/Qwen3-VL-8B-Instruct
• Parameters: 8.7 billion
• Architecture: Qwen3-VL (Vision-Language)
• Context Length: 262,144 tokens
• Vision Encoder: ViT-based with spatial merge

Training Data

Dataset: WayBob/Disaster_Recognition_RemoteSense_EN_CN_JA

This dataset is organized and prepared from the xView2 building damage assessment challenge, adapted for disaster type recognition tasks.

Split	Samples	Languages	Coverage
Training	55,008	EN/JA/ZH	All disaster types
Test	5,598	EN/JA/ZH	Held-out evaluation
Total	60,606	Trilingual	Global disasters

Disaster Types Covered:

• 🔥 Fire/Wildfire
• 🌊 Flood
• 🌀 Hurricane/Wind damage
• 🏚️ Earthquake
• 🌊 Tsunami
• 🌋 Volcano

Geographic Coverage: Global dataset including disasters from North America, Asia, Europe, and other regions

Data Format: Post-disaster satellite/aerial imagery with corresponding disaster type annotations in three languages (English, Japanese, Chinese)

Training Configuration

Hardware:

• GPU: NVIDIA RTX 4090 24GB
• Framework: LLaMA-Factory

Hyperparameters:

method: qlora_4bit
quantization: 4-bit (BitsAndBytes)
lora_rank: 8
lora_alpha: 16
lora_target: all (all linear layers)
lora_dropout: 0.05
learning_rate: 1.0e-4
batch_size: 4 per device × 2 accumulation = 8 effective
epochs: 3.0
optimizer: AdamW
lr_scheduler: cosine
warmup_ratio: 0.1
precision: bfloat16
image_max_pixels: 262144

Training Duration: ~6.4 hours (23,190 seconds)

Memory Usage: ~10-12GB VRAM

Training Results

Metric	Value
Final Training Loss	0.0239
Training Throughput	7.12 samples/second
Total Training Steps	10,314
Training Duration	~6.4 hours

Training Loss Curve:

Model Comparison Examples

Comparison between base model and fine-tuned model on disaster type recognition tasks. The following examples demonstrate significant improvements in accuracy and response quality.

Example 1: Fire Disaster (Southern California)

Image: socal-fire_00001128_post_disaster.png

Question (English): "What type of disaster occurred in this image?"

Model	Response
Base Model	"Based on the visual evidence in the image, this appears to be the aftermath of a landslide or rockfall... The debris at the bottom is the key indicator..." ❌ INCORRECT - Misidentified as landslide (acceptable?)
Fine-tuned	"Fire disaster" ✅ CORRECT - Accurate and concise
Ground Truth	"A fire disaster occurred in this image."

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Example 2: Flood Disaster (Hurricane Harvey)

Image: hurricane-harvey_00000186_post_disaster.png

Question (English): "What type of disaster occurred in this image?"

Model	Response
Base Model	"The image shows a green algae bloom, which is not a traditional disaster like a hurricane or earthquake, but it can be considered an environmental disaster..." ❌ INCORRECT - Completely misidentified
Fine-tuned	"Flood disaster" ✅ CORRECT
Ground Truth	"A flooding disaster occurred in this image."

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Example 3: Flood Disaster (Hurricane Florence)

Image: hurricane-florence_00000145_post_disaster.png

Question (English): "What type of disaster occurred in this image?"

Model	Response
Base Model	"Based on the image provided, there is no visible evidence of a disaster... The scene appears to be a normal, undisturbed landscape." ❌ INCORRECT - Failed to detect disaster
Fine-tuned	"Flooding" ✅ CORRECT
Ground Truth	"A flooding disaster occurred in this image."

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Example 4: Wind Disaster (Hurricane Michael) - Japanese

Image: hurricane-michael_00000436_post_disaster.png

Question (Japanese): "この画像ではどのような種類の災害が発生しましたか？"

Model	Response
Base Model	"この画像では洪水が発生したと推測されます..." ❌ INCORRECT - Misidentified as flood
Fine-tuned	"この画像では風災災害が発生しました。" ✅ CORRECT
Ground Truth	"この画像では風災災害が発生しました。"

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Example 5: Wind Disaster (Hurricane Michael) - Japanese

Image: hurricane-michael_00000197_post_disaster.png

Question (Japanese): "この画像ではどのような種類の災害が発生しましたか？"

Model	Response
Base Model	"この画像では台風による災害が発生しています。画像に見られるのは、台風の影響で甚大な被害を受けた都市部の衛星写真です..." ⚠️ PARTIALLY CORRECT - Correct but overly verbose (acceptable)
Fine-tuned	"この画像では風災災害が発生しました。" ✅ CORRECT - Accurate and concise
Ground Truth	"この画像では風災災害が発生しました。"

Key Improvements

The fine-tuned model demonstrates significant improvements over the base model:

• ✅ Accurate Disaster Type Recognition - Correctly identifies specific disaster types
• ✅ Concise Responses - Provides direct answers without unnecessary verbosity
• ✅ Eliminated Hallucinations - No longer invents non-existent disaster details
• ✅ Consistent Multilingual Performance - Reliable across English, Japanese, and Chinese
• ✅ Reduced Misidentification - Accurately distinguishes between different disaster types

Use Cases

Emergency Response & Humanitarian Aid

• Rapid Damage Assessment: Quickly identify disaster types from satellite imagery
• Resource Allocation: Prioritize aid based on disaster type recognition
• Disaster Mapping: Automatically tag disaster types in large image datasets
• Multi-language Support: Works with international teams (EN/JA/ZH)

Research & Analysis

• Disaster Dataset Annotation: Accelerate labeling of disaster imagery
• Historical Analysis: Classify historical disaster images
• Climate Impact Studies: Track disaster type distributions over time
• Cross-lingual Research: Unified model for international collaborations

Monitoring & Early Warning

• Satellite Monitoring: Automated disaster type identification from satellite feeds
• Damage Verification: Confirm disaster types reported by ground teams
• Multi-source Intelligence: Integrate with other disaster detection systems

Training Reproduction

Training Configuration File

# examples/train_qlora/qwen3vl_8b_xview2_4bit.yaml
model_name_or_path: Qwen/Qwen3-VL-8B-Instruct
quantization_bit: 4
quantization_method: bnb
image_max_pixels: 262144
video_max_pixels: 16384
trust_remote_code: true

stage: sft
do_train: true
finetuning_type: lora
lora_rank: 8
lora_alpha: 16
lora_target: all
lora_dropout: 0.05

dataset: xview2_disaster
eval_dataset: xview2_disaster_test
template: qwen3_vl_nothink
cutoff_len: 2048
max_samples: 55008
preprocessing_num_workers: 16

output_dir: saves/qwen3vl-8b/xview2/lora/sft
save_steps: 500
plot_loss: true
report_to: wandb

per_device_train_batch_size: 4
gradient_accumulation_steps: 2
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true

Run Training

llamafactory-cli train examples/train_qlora/qwen3vl_8b_xview2_4bit.yaml

Model Files

Model Weights & Config

• adapter_config.json - LoRA adapter configuration
• adapter_model.safetensors - LoRA adapter weights (~22MB)
• training_args.bin - Training arguments

Training Results

• training_loss.png - Training loss curve
• trainer_log.jsonl - Detailed training logs
• all_results.json - Final training metrics
• train_results.json - Training statistics

Checkpoints

21 intermediate checkpoints saved every 500 steps:

• checkpoint-500/ through checkpoint-10000/
• checkpoint-10314/ (final checkpoint)

You can load any checkpoint by specifying its path in the adapter_name_or_path parameter.

Limitations

• Language: Primarily trained on English/Japanese/Chinese; performance on other languages not guaranteed
• Domain: Specialized for post-disaster satellite/aerial imagery; may not work on ground-level photos
• Disaster Type Coverage: Some disaster types may have limited training samples, affecting recognition accuracy
• Quantization: Designed for 4-bit quantization; full precision inference not tested
• Geographic Bias: Training data may not cover all geographic regions equally
• Model Evaluation: Comprehensive evaluation is ongoing; performance metrics will be updated

Intended Use Cases

✅ Recommended:

• Post-disaster satellite/aerial image analysis
• Disaster type classification for emergency response
• Automated disaster dataset annotation
• Multilingual disaster recognition (EN/JA/ZH)
• Research on disaster impact assessment

❌ Not Recommended:

• Real-time disaster prediction (this is classification, not prediction)
• Ground-level disaster assessment (trained on aerial imagery)
• Medical emergency classification
• Legal/insurance claim decisions without human verification
• Fine-grained damage severity assessment (binary disaster type only)

Ethical Considerations

Responsible Use

• Human Oversight Required: This model should augment, not replace, human disaster assessment
• Verification Needed: All classifications should be verified by disaster response professionals
• Not for Sole Decision-Making: Do not use as the only basis for resource allocation or policy decisions
• Privacy: Be mindful of privacy when processing imagery that may contain identifiable information
• Bias Awareness: Model performance may vary across geographic regions and disaster contexts

Humanitarian Applications

This model is intended to support humanitarian efforts and disaster response. We encourage:

• Open collaboration with disaster response organizations
• Responsible sharing of insights with affected communities
• Transparent communication of model limitations
• Continuous improvement based on real-world feedback

Citation

@misc{qwen3vl-8b-qlora-xview2-disaster,
  author = {WayBob},
  title = {Qwen3VL-8B QLora 4-bit xView2 Disaster Recognition},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/WayBob/Qwen3VL-8B-QLora-4bit-xView2-Disaster-Recognition}
}

@misc{disaster-recognition-dataset,
  title={Disaster Recognition RemoteSense Dataset (EN/CN/JA)},
  author={WayBob},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/WayBob/Disaster_Recognition_RemoteSense_EN_CN_JA}
}

@inproceedings{xview2,
  title={xBD: A Dataset for Assessing Building Damage from Satellite Imagery},
  author={Gupta, Ritwik and Hosfelt, Richard and Sajeev, Sandra and Patel, Nirav and Goodman, Bryce and Doshi, Jigar and Heim, Eric and Choset, Howie and Gaston, Matthew},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year={2019}
}

Acknowledgements

Base Model:

• Qwen3-VL-8B-Instruct - Alibaba Cloud Qwen Team
• Licensed under Apache 2.0

Dataset:

• WayBob/Disaster_Recognition_RemoteSense_EN_CN_JA - Trilingual disaster recognition dataset
• Organized and prepared from xView2 building damage assessment challenge
• Original xView2 dataset by DIUx (Defense Innovation Unit)
• Licensed under Creative Commons

Training Framework:

• LLaMA-Factory by hiyouga

Method:

• QLoRA: Efficient Finetuning of Quantized LLMs by Dettmers et al.

Infrastructure:

• NVIDIA RTX 4090 24GB GPU

License

This model is licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0).

Key License Terms

• Share: You can copy and redistribute the material in any medium or format for any purpose, even commercially
• Adapt: You can remix, transform, and build upon the material for any purpose, even commercially
• Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made
• No Additional Restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits

Full License: See CC-BY-4.0 License for complete terms.

Contact

• HuggingFace: WayBob
• Model Repository: Qwen3VL-8B-QLora-4bit-xView2-Disaster-Recognition
• Issues: Please report issues or suggestions through HuggingFace discussions

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Disclaimer: This model is provided for research and humanitarian purposes. Always verify model outputs with domain experts before making critical decisions based on disaster classifications.