Kunpeng
4dee7f664d
- Track only trained best.pt under runs/*/weights; ignore epoch/last checkpoints, downloaded base weights, and server logs (.gitignore) - Move runs/detect/runs/detect/smoke_fire_yolo26n_v2-3 to runs/detect/smoke_fire_yolo26n_v2-3 and update docs/model_comparison.md - Replace the Git LFS claim with an accurate plain-file statement
2.2 KiB
Model Training Comparison
This repository includes the best available smoke and fire detection weights and the evaluation artifacts needed to inspect the completed run. Trained weights are committed as regular files (only best.pt, no checkpoints or pretrained base models).
Available runs
| Model | Run | Best epoch | Precision | Recall | mAP50 | mAP50-95 | Best weight |
|---|---|---|---|---|---|---|---|
| YOLO11s | smoke_fire_yolo11s_v1-4 |
84 | 0.77349 | 0.72164 | 0.77499 | 0.45688 | runs/detect/smoke_fire_yolo11s_v1-4/weights/best.pt |
| YOLO26n | smoke_fire_yolo26n_v2-3 |
Not available | Not available | Not available | Not available | Not available | runs/detect/smoke_fire_yolo26n_v2-3/weights/best.pt |
The YOLO11s row uses epoch 84 from results.csv, selected by the highest metrics/mAP50-95(B). Training completed 124 epochs before early stopping. The best observed mAP50 was 0.77720 at epoch 90, while the selected checkpoint maximizes mAP50-95.
The YOLO26n directory currently contains only best.pt; its args.yaml and results.csv are unavailable. Its detection quality therefore cannot be compared fairly with YOLO11s yet. The weight is included as a deployment-size baseline only.
Included evidence
The YOLO11s run includes:
- Full epoch metrics in
results.csv - Training and validation summary in
results.png - Precision, recall, F1, and PR curves
- Raw and normalized confusion matrices
- Validation label and prediction previews
- Effective training parameters in
args.yaml - Best-checkpoint metadata in
best_point.json
Periodic epoch*.pt checkpoints, last.pt, training batch previews, runtime server logs, and manual audit preview images are intentionally excluded. They are either reproducible intermediates or unrelated to comparing the best models.
Reproducing evaluation
Run validation against the configured smoke/fire dataset:
uv run fire-yolo val --weights runs/detect/smoke_fire_yolo11s_v1-4/weights/best.pt
To complete the YOLO26n comparison, rerun validation with its best weight and retain the generated args.yaml, results.csv, plots, and confusion matrices beside the checkpoint.