diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index f06ff32dbe..7e035b3f23 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -2080,15 +2080,17 @@ void ONNXImporter::parseBatchNormalization(LayerParams& layerParams, const openc
     addLayer(layerParams, node_proto);
 }
 
+// A * B + C = Y, we require that the dimension of A is [m, k], and the dimension of B is [n, k].
+// And the dim of output Y is [m, n]
 void ONNXImporter::parseGemm(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {
     CV_Assert(node_proto.input_size() >= 2);
     layerParams.type = "InnerProduct";
     Mat weights = getBlob(node_proto, 1);
-    int ind_num_out = 0;
-    if (layerParams.has("transB") && !layerParams.get<int>("transB")) {
+
+    if (!layerParams.get<int>("transB", 0))
+    {
         transpose(weights, weights);
-        ind_num_out = 1;
     }
     layerParams.blobs.push_back(weights);
 
@@ -2110,7 +2112,7 @@ void ONNXImporter::parseGemm(LayerParams& layerParams, const opencv_onnx::NodePr
         addLayer(constParams, proto);
     }
 
-    layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]);
+    layerParams.set("num_output", layerParams.blobs[0].size[0]);
     layerParams.set("bias_term", node_proto.input_size() == 3);
     addLayer(layerParams, node_proto);
 }
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 50540cd043..bf59cbba97 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -1746,6 +1746,11 @@ TEST_P(Test_ONNX_layers, DivConst)
     testONNXModels("div_const");
 }
 
+TEST_P(Test_ONNX_layers, Gemm)
+{
+    testONNXModels("gemm_no_transB");
+    testONNXModels("gemm_transB_0");
+}
 
 TEST_P(Test_ONNX_layers, Quantized_Convolution)
 {