Fire-Detection-Siglip2
| Property | Value |
|---|---|
| Base Model | google/siglip2-base-patch16-224 |
| Task Type | Image Classification |
| Model URL | Hugging Face |
| Accuracy | 99.41% |
What is Fire-Detection-Siglip2?
Fire-Detection-Siglip2 is a specialized vision-language model fine-tuned from the SiglipForImageClassification architecture. It excels at detecting and classifying images into three distinct categories: fire, smoke, or normal conditions, achieving remarkable accuracy across all classes.
Implementation Details
The model utilizes the transformers library and can be easily integrated into existing pipelines. It processes RGB images and returns probability scores for each classification category. With precision scores of 99.40% for fire detection, 98.92% for normal conditions, and 99.90% for smoke detection, it demonstrates exceptional reliability across all classification tasks.
- Built on SiglipForImageClassification architecture
- Processes images through a specialized image processor
- Returns probability distributions across three classes
- Implements torch-based inference pipeline
Core Capabilities
- Real-time fire detection in image data
- Smoke presence identification
- Normal condition verification
- Integration with surveillance systems
- Support for automated monitoring solutions
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional accuracy across all three classification categories (fire, smoke, and normal conditions) makes it particularly reliable for safety-critical applications. Its high precision scores (99.40% for fire, 98.92% for normal, and 99.90% for smoke) demonstrate its robustness in real-world scenarios.
Q: What are the recommended use cases?
The model is ideal for fire safety monitoring, early warning systems, disaster prevention, and smart home IoT integration. It can be implemented in surveillance systems, automated monitoring solutions, and emergency response applications where reliable fire and smoke detection is crucial.
