VAE_ResNet_3D

Select with config.model.set_model('VAE_ResNet_3D'). The table shows the actual defaults for this model variant. A dash in Default search means the parameter stays fixed unless you assign a compatible distribution through config.model.search.

Parameter	Type	Default	Meaning / values	Default search
config.model.parameters.n_res_blocks	int	4	Number of residual blocks per level.	IntRange(low=4, high=5, step=1, log=False)
config.model.parameters.n_levels	int	4	Number of encoder and decoder levels.	—
config.model.parameters.z_channels	int	64	Channel count in the spatial bottleneck.	Choice(values=(64, 128))
config.model.parameters.bottleneck_dim	int	128	Dimension of the latent vector.	Choice(values=(128, 256))
config.model.parameters.use_multires_skips	bool	True	Use encoder features from multiple resolutions as skip connections.	—
config.model.parameters.recon_weight	float	100.0	Weight of the reconstruction loss.	—
config.model.parameters.beta_kl_start	float	0.0	Initial weight of the KL loss.	—
config.model.parameters.beta_kl_max	float	0.05	Maximum weight of the KL loss.	—
config.model.parameters.beta_kl_warmup_start	int	20	Epoch at which the KL weight begins to increase.	—
config.model.parameters.beta_kl_warmup_epochs	int	30	Number of epochs needed to reach beta_kl_max.	—
config.model.parameters.free_bits	float	0.0	KL free-bits allowance for latent dimensions.	—
config.model.parameters.recon_loss	str	'mse'	Reconstruction loss, such as 'mse' or 'smoothl1'.	—
config.model.parameters.recon_smoothl1_beta	float	1.0	Transition point of the Smooth L1 loss.	—
config.model.parameters.use_transpose_conv	bool	False	Use transposed convolutions for upsampling.	—
config.model.parameters.fg_weight	float	1.0	Additional weight for foreground pixels in the reconstruction loss.	—
config.model.parameters.fg_threshold	float	0.0	Threshold for selecting foreground pixels.	—