diff --git a/resolve/re/likelihood.py b/resolve/re/likelihood.py
index 27d9fe571a57abb556090572578a1684772dc3c8..782c9d87de4870d624119ff6e9b0d9f77084e637 100644
--- a/resolve/re/likelihood.py
+++ b/resolve/re/likelihood.py
@@ -9,10 +9,8 @@ from .likelihood_models import *
 # The classes from .likelihoods model are:
 #  - ModelCalibrationLikelihoodFixedCovariance
 #  - ModelCalibrationLikelihoodVariableCovariance
-#  - ModelImagingLikelihoodFixedCovarianceCalibrationField
-#  - ModelImagingLikelihoodFixedCovarianceCalibrationOperator
-#  - ModelImagingLikelihoodVariableCovarianceCalibrationField
-#  - ModelImagingLikelihoodVariableCovarianceCalibrationOperator
+#  - ModelImagingLikelihoodFixedCovariance
+#  - ModelImagingLikelihoodVariableCovariance
 
 from ..util import _obj2list, _duplicate
 from ..data.observation import Observation
@@ -81,16 +79,20 @@ def CalibrationLikelihood(
         flagged_data = jnp.asarray(oo.vis.val)[mask]
 
         if log_inv_cov is None:
+            if label is None:
+                print(f"| Imaging Likelihood {ii} |")
+            else:
+                print(f"| Imaging Likelihood {ii} | {label} |")
+
             model = ModelCalibrationLikelihoodFixedCovariance(cop,model_vis,mask)
             flagged_inv_cov = jnp.asarray(oo.weight.val)[mask]
             
-            lh = jft.Gaussian(data=flagged_data, noise_cov_inv=flagged_inv_cov)
-        
+            lh = jft.Gaussian(data=flagged_data, noise_cov_inv=flagged_inv_cov) 
         else:
             model = ModelCalibrationLikelihoodVariableCovariance(cop,model_vis,log_inv_cov,mask)
 
             lh = jft.VariableCovarianceGaussian(data=flagged_data,iscomplex=jnp.iscomplexobj(oo.vis.val))
-        
+
         lh_with_model = lh.amend(model)
         lh_with_model._domain = jft.Vector(lh_with_model._domain)
 
@@ -202,12 +204,16 @@ def ImagingLikelihood(
         flagged_data = jnp.asarray(oo.vis.val)[mask]
 
         if log_inv_cov is None:
+            if label is None:
+                print(f"| Imaging Likelihood {ii} |")
+            else:
+                print(f"| Imaging Likelihood {ii} | {label} |")
+
             model = ModelImagingLikelihoodFixedCovariance(R,sky_operator,mask,cop,cfld)
 
             flagged_inv_cov = jnp.asarray(oo.weight.val)[mask]
         
             lh = jft.Gaussian(data=flagged_data, noise_cov_inv=flagged_inv_cov)
-
         else:
             model = ModelImagingLikelihoodVariableCovariance(R,sky_operator,log_inv_cov,mask,cop,cfld)