neuralib.deeplabcut.core.DeepLabCutModelConfig
- class neuralib.deeplabcut.core.DeepLabCutModelConfig[source]
Bases:
TypedDictDeepLabCut model configuration
- __init__(*args, **kwargs)
Methods
__init__(*args, **kwargs)clear()copy()fromkeys(iterable[, value])Create a new dictionary with keys from iterable and values set to value.
get(key[, default])Return the value for key if key is in the dictionary, else default.
items()keys()pop(k[,d])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.
setdefault(key[, default])Insert key with a value of default if key is not in the dictionary.
update([E, ]**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()Attributes
- 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