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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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31
ultralytics/models/yolo/segment/train.py
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@ -26,12 +26,12 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
"""Initialize a SegmentationTrainer object with given arguments."""
if overrides is None:
overrides = {}
overrides['task'] = 'segment'
overrides["task"] = "segment"
super().__init__(cfg, overrides, _callbacks)
def get_model(self, cfg=None, weights=None, verbose=True):
"""Return SegmentationModel initialized with specified config and weights."""
model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
if weights:
model.load(weights)
@ -39,22 +39,23 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
def get_validator(self):
"""Return an instance of SegmentationValidator for validation of YOLO model."""
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
return yolo.segment.SegmentationValidator(self.test_loader,
save_dir=self.save_dir,
args=copy(self.args),
_callbacks=self.callbacks)
self.loss_names = "box_loss", "seg_loss", "cls_loss", "dfl_loss"
return yolo.segment.SegmentationValidator(
self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
)
def plot_training_samples(self, batch, ni):
"""Creates a plot of training sample images with labels and box coordinates."""
plot_images(batch['img'],
batch['batch_idx'],
batch['cls'].squeeze(-1),
batch['bboxes'],
masks=batch['masks'],
paths=batch['im_file'],
fname=self.save_dir / f'train_batch{ni}.jpg',
on_plot=self.on_plot)
plot_images(
batch["img"],
batch["batch_idx"],
batch["cls"].squeeze(-1),
batch["bboxes"],
masks=batch["masks"],
paths=batch["im_file"],
fname=self.save_dir / f"train_batch{ni}.jpg",
on_plot=self.on_plot,
)
def plot_metrics(self):
"""Plots training/val metrics."""