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Add HUB-SDK Docs reference section (#7781)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn Jocher 2024-01-27 00:21:31 +01:00 • committed by GitHub
parent 5941128835
commit ee88882874
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25 changed files with 142 additions and 47 deletions
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7
ultralytics/engine/results.py
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@ -667,8 +667,11 @@ class OBB(BaseTensor):
@property
@lru_cache(maxsize=2)
def xyxy(self):
"""Return the horizontal boxes in xyxy format, (N, 4)."""
# This way to fit both torch and numpy version
"""
Return the horizontal boxes in xyxy format, (N, 4).
Accepts both torch and numpy boxes.
"""
x1 = self.xyxyxyxy[..., 0].min(1).values
x2 = self.xyxyxyxy[..., 0].max(1).values
y1 = self.xyxyxyxy[..., 1].min(1).values

8
ultralytics/engine/trainer.py
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@ -563,8 +563,12 @@ class BaseTrainer:
raise NotImplementedError("build_dataset function not implemented in trainer")
def label_loss_items(self, loss_items=None, prefix="train"):
"""Returns a loss dict with labelled training loss items tensor."""
# Not needed for classification but necessary for segmentation & detection
"""
Returns a loss dict with labelled training loss items tensor.
Note:
This is not needed for classification but necessary for segmentation & detection
"""
return {"loss": loss_items} if loss_items is not None else ["loss"]
def set_model_attributes(self):