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Update `overlap_mask` description. (#17324)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
pull/17330/head^2
Mohammed Yasin 3 weeks ago committed by GitHub
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  1. 2
      docs/en/macros/train-args.md
  2. 2
      ultralytics/cfg/default.yaml

2
docs/en/macros/train-args.md
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@ -43,7 +43,7 @@
| `kobj` | `2.0` | Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy. | | `kobj` | `2.0` | Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy. |
| `label_smoothing` | `0.0` | Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization. | | `label_smoothing` | `0.0` | Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization. |
| `nbs` | `64` | Nominal batch size for normalization of loss. | | `nbs` | `64` | Nominal batch size for normalization of loss. |
| `overlap_mask` | `True` | Determines whether segmentation masks should overlap during training, applicable in [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks. | | `overlap_mask` | `True` | Determines whether object masks should be merged into a single mask for training, or kept separate for each object. In case of overlap, the smaller mask is overlayed on top of the larger mask during merge. |
| `mask_ratio` | `4` | Downsample ratio for segmentation masks, affecting the resolution of masks used during training. | | `mask_ratio` | `4` | Downsample ratio for segmentation masks, affecting the resolution of masks used during training. |
| `dropout` | `0.0` | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training. | | `dropout` | `0.0` | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training. |
| `val` | `True` | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset. | | `val` | `True` | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset. |

2
ultralytics/cfg/default.yaml
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@ -36,7 +36,7 @@ profile: False # (bool) profile ONNX and TensorRT speeds during training for log
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation # Segmentation
overlap_mask: True # (bool) masks should overlap during training (segment train only) overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only) mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification # Classification
dropout: 0.0 # (float) use dropout regularization (classify train only) dropout: 0.0 # (float) use dropout regularization (classify train only)

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