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ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-17 19:10:20 +02:00
committed by GitHub
parent b1119d512e
commit 23fc50641c
92 changed files with 378 additions and 206 deletions
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7
ultralytics/yolo/v8/detect/train.py
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@ -121,17 +121,18 @@ class DetectionTrainer(BaseTrainer):
cls=batch['cls'].squeeze(-1),
bboxes=batch['bboxes'],
paths=batch['im_file'],
fname=self.save_dir / f'train_batch{ni}.jpg')
fname=self.save_dir / f'train_batch{ni}.jpg',
on_plot=self.on_plot)
def plot_metrics(self):
"""Plots metrics from a CSV file."""
plot_results(file=self.csv) # save results.png
plot_results(file=self.csv, on_plot=self.on_plot) # save results.png
def plot_training_labels(self):
"""Create a labeled training plot of the YOLO model."""
boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
# Criterion class for computing training losses