leon-se/ForestFireVLM-3B
ForestFireVLM-3B is a 3 billion parameter vision-language model developed by leon-se, fine-tuned from Qwen2.5-VL-3B-Instruct. This specialized model is designed for forest fire detection and analysis, excelling at identifying smoke, flames, fire characteristics, and hazards from aerial imagery. It leverages a 32768 token context length for comprehensive visual data processing, making it ideal for environmental monitoring and disaster response applications.
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Overview
ForestFireVLM-3B is a specialized 3 billion parameter vision-language model (VLM) developed by leon-se. It is fine-tuned from Qwen2.5-VL-3B-Instruct using the LLaMA-Factory framework, specifically optimized for forest fire detection and analysis tasks. This model processes aerial imagery to identify critical fire-related elements.
Key Capabilities
- Forest Fire Detection: Accurately identifies the presence of forest fires.
- Smoke and Flame Recognition: Distinguishes and analyzes smoke plumes and visible flames.
- Fire Characteristics Analysis: Assesses various attributes of detected fires.
- Hazard Identification: Pinpoints potential hazards associated with forest fires from visual data.
- High Context Processing: Utilizes a 32768 token context length for detailed image analysis.
Good for
- Environmental Monitoring: Ideal for automated surveillance of forests for early fire detection.
- Disaster Response: Supports rapid assessment of fire situations for emergency services.
- Aerial Imagery Analysis: Specialized in processing and interpreting visual data from drones or satellites for fire management.
Evaluations for ForestFireVLM-3B were conducted using a dedicated codebase available on GitHub and the ForestFireInsights-Eval dataset.