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encoder_hidden_sizes (`List`, *optional*, defaults to `[64, 64, 128, 128]`):
    The number of channels in each convolutional layer in the encoder.
keypoint_decoder_dim (`int`, *optional*, defaults to 65):
    The output dimension of the keypoint decoder.
descriptor_decoder_dim (`int`, *optional*, defaults to 256):
    The output dimension of the descriptor decoder.
keypoint_threshold (`float`, *optional*, defaults to 0.005):
    The threshold to use for extracting keypoints.
max_keypoints (`int`, *optional*, defaults to -1):
    The maximum number of keypoints to extract. If `-1`, will extract all keypoints.
nms_radius (`int`, *optional*, defaults to 4):
    The radius for non-maximum suppression.
border_removal_distance (`int`, *optional*, defaults to 4):
    The distance from the border to remove keypoints.

Example:
```python
>>> from transformers import SuperPointConfig, SuperPointForKeypointDetection

>>> # Initializing a SuperPoint superpoint style configuration
>>> configuration = SuperPointConfig()
>>> # Initializing a model from the superpoint style configuration
>>> model = SuperPointForKeypointDetection(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```Ú
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__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r   r   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r   s   @r   r   r      sM   ø‡ € ñð8 €Jà8JÐØ"ÐØ "ÐØ"%ÐØ %ÐØ€MØ€JØ#$ÐØ#Ð÷S ƒ r   r   N)Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r    r   r   Ú<module>r1      sE   ðõ  /å 3Ý #ñ Ð<Ô=Øô'$Ð'ó '$ó ó >ð'$ðT Ð
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