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EomtConfiga~  
layerscale_value (`float`, *optional*, defaults to 1.0):
    Initial value for the LayerScale parameter.
num_upscale_blocks (`int`, *optional*, defaults to 2):
    Number of upsampling blocks used in the decoder or segmentation head.
use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
    Whether to use the SwiGLU feedforward neural network.
num_blocks (`int`, *optional*, defaults to 4):
    Number of feature blocks or stages in the architecture.
no_object_weight (`float`, *optional*, defaults to 0.1):
    Loss weight for the 'no object' class in panoptic/instance segmentation.
class_weight (`float`, *optional*, defaults to 2.0):
    Loss weight for classification targets.
mask_weight (`float`, *optional*, defaults to 5.0):
    Loss weight for mask prediction.
train_num_points (`int`, *optional*, defaults to 12544):
    Number of points to sample for mask loss computation during training.
oversample_ratio (`float`, *optional*, defaults to 3.0):
    Oversampling ratio used in point sampling for mask training.
importance_sample_ratio (`float`, *optional*, defaults to 0.75):
    Ratio of points to sample based on importance during training.
num_queries (`int`, *optional*, defaults to 200):
    Number of object queries in the Transformer.
num_register_tokens (`int`, *optional*, defaults to 4):
    Number of learnable register tokens added to the transformer input.

Example:

```python
>>> from transformers import EomtConfig, EomtForUniversalSegmentation

>>> # Initialize configuration
>>> config = EomtConfig()

>>> # Initialize model
>>> model = EomtForUniversalSegmentation(config)

>>> # Access config
>>> config = model.config
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hidden_actÚhidden_dropout_probÚinitializer_rangeÚlayer_norm_epsÚ
image_sizeÚ
patch_sizeÚnum_channelsÚ	mlp_ratioÚlayerscale_valueÚdrop_path_rateÚnum_upscale_blocksÚattention_dropoutÚuse_swiglu_ffnÚ
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__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
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