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AutoConfigz%facebook/dinov3-vitl16-chmv2-dpt-head)Ú
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backbone_config (`Union[dict, "PreTrainedConfig"]`, *optional*):
    The configuration of the backbone model. Only DINOv3ViTConfig is currently supported.
patch_size (`int`, *optional*, defaults to 16):
    The patch size used by the backbone vision transformer.
reassemble_factors (`list[float]`, *optional*, defaults to `[4, 2, 1, 0.5]`):
    The up/downsampling factors of the reassemble layers.
post_process_channels (`list[int]`, *optional*, defaults to `[128, 256, 512, 1024]`):
    The output channel sizes of the reassemble stage for each backbone feature level.
fusion_hidden_size (`int`, *optional*, defaults to 256):
    The number of channels before fusion.
head_hidden_size (`int`, *optional*, defaults to 128):
    The number of channels in the hidden layer of the depth estimation head.
number_output_channels (`int`, *optional*, defaults to 256):
    Number of output channels for the CHMv2 head (number of depth bins).
readout_type (`str`, *optional*, defaults to `"project"`):
    Type of readout operation for the CLS token. One of `["ignore", "add", "project"]`.
min_depth (`float`, *optional*, defaults to 0.001):
    The minimum depth value for depth bin calculation.
max_depth (`float`, *optional*, defaults to 96.0):
    The maximum depth value for depth bin calculation.
bins_strategy (`str`, *optional*, defaults to `"chmv2_mixlog"`):
    The strategy for depth bins distribution. One of `["linear", "log", "chmv2_mixlog"]`.
norm_strategy (`str`, *optional*, defaults to `"chmv2_mixlog"`):
    The normalization strategy for depth prediction. One of `["linear", "softmax", "sigmoid", "chmv2_mixlog"]`.

```python
>>> from transformers import CHMv2Config, CHMv2ForDepthEstimation

>>> configuration = CHMv2Config()
>>> model = CHMv2ForDepthEstimation(configuration)
>>> configuration = model.config
```
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