Spaces:
Sleeping
Sleeping
title: Submission Template | |
emoji: 🔥 | |
colorFrom: yellow | |
colorTo: green | |
sdk: docker | |
pinned: false | |
# Yolo V8 for smoke detection | |
## Model Description | |
This is a simple YOLO V8 model for the Frugal AI Challenge 2024, specifically for the smoke detection challenge. | |
### Intended Use | |
- **Primary intended uses**: First submission of a non-trivial model | |
- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge | |
- **Out-of-scope use cases**: Not intended for production use or real-world detection tasks | |
## Training Data | |
The model the Pyro-SDIS Subset contains 33,636 images, including: | |
- 28,103 images with smoke | |
- 31,975 smoke instances | |
### Labels | |
0. Smoke | |
## Performance | |
### Metrics | |
- **Accuracy**: Still to estimate | |
- **Environmental Impact**: | |
- Emissions tracked in gCO2eq | |
- Energy consumption tracked in Wh | |
### Model Architecture | |
YOLO V8 | |
## Environmental Impact | |
Environmental impact is tracked using CodeCarbon, measuring: | |
- Carbon emissions during inference | |
- Energy consumption during inference | |
This tracking helps establish a baseline for the environmental impact of model deployment and inference. | |
## Limitations | |
- Not suitable for any real-world applications | |
## Ethical Considerations | |
- Environmental impact is tracked to promote awareness of AI's carbon footprint | |
``` | |