ultralytics 8.0.108
add Meituan YOLOv6 models (#2811)
Co-authored-by: Michael Currie <mcurrie@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Hicham Talaoubrid <98521878+HichTala@users.noreply.github.com> Co-authored-by: Zlobin Vladimir <vladimir.zlobin@intel.com> Co-authored-by: Szymon Mikler <sjmikler@gmail.com>
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@ -331,12 +331,12 @@ class YOLO:
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overrides = self.overrides.copy()
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overrides.update(kwargs)
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overrides['mode'] = 'export'
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if overrides.get('imgsz') is None:
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overrides['imgsz'] = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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if overrides.get('batch') is None:
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overrides['batch'] = 1 # default to 1 if not modified
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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if args.batch == DEFAULT_CFG.batch:
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args.batch = 1 # default to 1 if not modified
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return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
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def train(self, **kwargs):
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@ -684,12 +684,17 @@ def check_amp(model):
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im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
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prefix = colorstr('AMP: ')
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LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
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warning_msg = "Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False."
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try:
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from ultralytics import YOLO
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assert amp_allclose(YOLO('yolov8n.pt'), im)
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LOGGER.info(f'{prefix}checks passed ✅')
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except ConnectionError:
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LOGGER.warning(f"{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. Setting 'amp=True'.")
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LOGGER.warning(f'{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. {warning_msg}')
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except (AttributeError, ModuleNotFoundError):
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LOGGER.warning(
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f'{prefix}checks skipped ⚠️. Unable to load YOLOv8n due to possible Ultralytics package modifications. {warning_msg}'
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)
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except AssertionError:
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LOGGER.warning(f'{prefix}checks failed ❌. Anomalies were detected with AMP on your system that may lead to '
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f'NaN losses or zero-mAP results, so AMP will be disabled during training.')
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