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ultralytics 8.0.196 instance-mean Segment loss (#5285)

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Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-09 20:08:39 +02:00 • committed by GitHub
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6
docs/guides/hyperparameter-tuning.md
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@ -75,10 +75,10 @@ Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyp
```python
from ultralytics import YOLO
# Initialize the YOLO model
model = YOLO('yolov8n.pt')
# Tune hyperparameters on COCO8 for 30 epochs
model.tune(data='coco8.yaml', epochs=30, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
```
@ -123,7 +123,7 @@ This YAML file contains the best-performing hyperparameters found during the tun
# Best fitness metrics are {'metrics/precision(B)': 0.87247, 'metrics/recall(B)': 0.71387, 'metrics/mAP50(B)': 0.79106, 'metrics/mAP50-95(B)': 0.62651, 'val/box_loss': 2.79884, 'val/cls_loss': 2.72386, 'val/dfl_loss': 0.68503, 'fitness': 0.64297}
# Best fitness model is /usr/src/ultralytics/runs/detect/train498
# Best fitness hyperparameters are printed below.
lr0: 0.00269
lrf: 0.00288
momentum: 0.73375