diff --git a/src/mlj_helpers.jl b/src/mlj_helpers.jl
index c6b05bf..7311011 100644
--- a/src/mlj_helpers.jl
+++ b/src/mlj_helpers.jl
@@ -84,7 +84,7 @@ from_categorical(categorical) = MLJ.unwrap.(categorical)
 from_categorical(categorical::MLJ.Node) = MLJ.node(from_categorical, categorical)
 
 # transform a fitresult (containing only the model) back to a Fit containing the model and training scores
-to_fitresult(mach::MLJ.Machine{<:OD.Detector})::Fit = (mach.fitresult, mach.report.scores)
+to_fitresult(mach::MLJ.Machine{<:OD.Detector})::Fit = (mach.fitresult, MLJ.report(mach).scores)
 
 # this includes all composites defined in mlj_wrappers.jl
 const DetectorComposites = Union{
@@ -142,16 +142,27 @@ function augmented_transform(mach::MLJ.Machine{<:OD.Detector}; rows=:)
     return _augmented_transform(mach.model, to_fitresult(mach), selectrows(mach.model, rows, mach.data[1])...)
 end
 
+function get_scores_from_composite_report(mach)
+    # new #banana API
+    # fit_report = MLJ.report_given_method(mach)[:fit]
+    fit_report = mach.report
+    if haskey(fit_report, :additions) && haskey(fit_report.additions, :scores)
+        return fit_report.additions.scores
+    else
+        return fit_report.scores
+    end
+end
+
 function augmented_transform(mach::DetectorComposites; rows=:)
     check_mach(mach)
-    scores_train = mach.report.scores
+    scores_train = get_scores_from_composite_report(mach)
     scores_test = mach.fitresult.transform(selectrows(mach.model, rows, mach.data[1])...)
     return scores_train, scores_test
 end
 
 function augmented_transform(mach::DetectorSurrogates; rows=:)
     check_mach(mach)
-    scores_train = mach.report.scores
+    scores_train = get_scores_from_composite_report(mach)
     scores_test = mach.fitresult.transform(rows=rows)
     return scores_train, scores_test
 end
@@ -164,7 +175,7 @@ end
 
 function augmented_transform(mach::DetectorComposites, X)
     check_mach(mach)
-    scores_train = mach.report.scores
+    scores_train = get_scores_from_composite_report(mach)
     scores_test = mach.fitresult.transform(X)
     return scores_train, scores_test
 end