neuralib.tracking.deeplabcut.core.DeepLabCutModelConfig
- class neuralib.tracking.deeplabcut.core.DeepLabCutModelConfig[source]
Bases:
TypedDict- stride: float
- weigh_part_predictions: bool
- weigh_negatives: bool
- fg_fraction: float
- mean_pixel: list[float]
- shuffle: bool
- snapshot_prefix: str
- log_dir: str
- global_scale: float
- location_refinement: bool
- locref_stdev: float
- locref_loss_weight: float
- locref_huber_loss: bool
- optimizer: str
- intermediate_supervision: bool
- intermediate_supervision_layer: int
- regularize: bool
- weight_decay: float
- crop_pad: int
- scoremap_dir: str
- batch_size: int
- dataset_type: str
- deterministic: bool
- mirror: bool
- pairwise_huber_loss: bool
- weigh_only_present_joints: bool
- partaffinityfield_predict: bool
- pairwise_predict: bool
- all_joints: list[list[int]]
- all_joints_names: list[str]
- dataset: str
- init_weights: str
- net_type: str
- num_joints: int
- num_outputs: int
- __init__(*args, **kwargs)
- __new__(**kwargs)
- clear() None. Remove all items from D.
- copy() a shallow copy of D
- fromkeys(value=None, /)
Create a new dictionary with keys from iterable and values set to value.
- get(key, default=None, /)
Return the value for key if key is in the dictionary, else default.
- items() a set-like object providing a view on D's items
- keys() a set-like object providing a view on D's keys
- pop(k[, d]) v, remove specified key and return the corresponding value.
If the key is not found, return the default if given; otherwise, raise a KeyError.
- popitem()
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
- setdefault(key, default=None, /)
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
- update([E, ]**F) None. Update D from dict/iterable E and F.
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
- values() an object providing a view on D's values