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| 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 | |
| ``` | |