diff --git a/source/kann-metadata.json b/source/kann-metadata.json
index 1f4e72b47b..ba202ab0f2 100644
--- a/source/kann-metadata.json
+++ b/source/kann-metadata.json
@@ -7,30 +7,14 @@
         "name": "Substraction",
         "category": "Transform"
     },
-    {
-        "name": "Erf",
-        "category": "Layer"
-    },
     {
         "name": "LayerNorm",
         "category": "Normalization"
     },
-    {
-        "name": "Output",
-        "category": "Data"
-    },
-    {
-        "name": "MaxWithConstant",
-        "category": "Transform"
-    },
     {
         "name": "ConvolutionCV2",
         "category": "Layer"
     },
-    {
-        "name": "Reciprocal",
-        "category": "Layer"
-    },
     {
         "name": "ReLU",
         "category": "Activation"
@@ -43,14 +27,6 @@
         "name": "ResizeNearestAsymFloor",
         "category": "Shape"
     },
-    {
-        "name": "MulElementwise",
-        "category": "Transform"
-    },
-    {
-        "name": "Copy",
-        "category": "Transform"
-    },
     {
         "name": "Exp",
         "category": "Activation"
@@ -63,14 +39,6 @@
         "name": "Stddev",
         "category": "Normalization"
     },
-    {
-        "name": "Division",
-        "category": "Layer"
-    },
-    {
-        "name": "MatMul",
-        "category": "Transform"
-    },
     {
         "name": "AvgPooling",
         "category": "Pool"
@@ -79,78 +47,30 @@
         "name": "HardSigmoid",
         "category": "Activation"
     },
-    {
-        "name": "MaxElementwise",
-        "category": "Transform"
-    },
     {
         "name": "SeLU",
         "category": "Activation"
     },
     {
         "name": "LRN",
-        "category": "Activation"
-    },
-    {
-        "name": "MulWithConstant",
-        "category": "Transform"
-    },
-    {
-        "name": "Abs",
-        "category": "Transform"
-    },
-    {
-        "name": "Invert",
-        "category": "Transform"
-    },
-    {
-        "name": "Sqrt",
-        "category": "Transform"
-    },
-    {
-        "name": "MinElementwise",
-        "category": "Transform"
+        "category": "Normalization"
     },
     {
         "name": "GeLU",
         "category": "Activation"
     },
-    {
-        "name": "Input",
-        "category": "Data"
-    },
-    {
-        "name": "Logistic",
-        "category": "Activation"
-    },
     {
         "name": "Reshape",
         "category": "Shape"
     },
-    {
-        "name": "AddElementwise",
-        "category": "Transform"
-    },
-    {
-        "name": "Saturate",
-        "category": "Transform"
-    },
     {
         "name": "Tanh",
         "category": "Activation"
     },
-    {
-        "name": "ScalingVector",
-        "category": "Transform"
-    },
     {
         "name": "MaxPooling",
         "category": "Pool"
     },
-    {
-        "name": "Convert",
-        "category": "Transform"
-    },
     {
         "name": "DepthwiseConvolution",
         "category": "Layer"
@@ -159,14 +79,6 @@
         "name": "Softmax",
         "category": "Activation"
     },
-    {
-        "name": "MinWithConstant",
-        "category": "Transform"
-    },
-    {
-        "name": "WriteValue",
-        "category": "Data"
-    },
     {
         "name": "SiLU",
         "category": "Activation"
diff --git a/source/kann.js b/source/kann.js
index 2d624cf728..86de43e0e3 100644
--- a/source/kann.js
+++ b/source/kann.js
@@ -14,17 +14,6 @@ kann.ModelFactory = class {
             context.target = reader;
             return;
         }
-        // If KaNN identifier not found, check if the file is '.kann'
-        const identifier = context.identifier;
-        const extension = identifier.split('.').pop().toLowerCase();
-        if (extension === 'kann' && reader && reader.identifier === '') {
-            const version = reader.uint32_(reader.root, 4, 0);
-            if (version === 1) {
-                context.type = 'kann.flatbuffers';
-                context.target = reader;
-                return;
-            }
-        }
     }
 
     // Open and initialize the KaNN model from the context