From 43053327ffabbd0dc205f65497f0086dd981b198 Mon Sep 17 00:00:00 2001
From: ramukhsuya <ayush.kumar.ece23@itbhu.ac.in>
Date: Fri, 19 Dec 2025 20:41:38 +0530
Subject: [PATCH] DNN: Add TFLite Minimum layer support

---
 modules/dnn/src/tflite/tflite_importer.cpp |  5 +++-
 modules/dnn/test/test_tflite_importer.cpp  | 28 ++++++++++++++++++++++
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/modules/dnn/src/tflite/tflite_importer.cpp b/modules/dnn/src/tflite/tflite_importer.cpp
index edcc3804e9..4b443ed797 100644
--- a/modules/dnn/src/tflite/tflite_importer.cpp
+++ b/modules/dnn/src/tflite/tflite_importer.cpp
@@ -288,7 +288,7 @@ TFLiteImporter::DispatchMap TFLiteImporter::buildDispatchMap()
     dispatch["ADD"] = dispatch["MUL"] = dispatch["SUB"] =
         dispatch["SQRT"] = dispatch["DIV"] = dispatch["NEG"] =
         dispatch["RSQRT"] = dispatch["SQUARED_DIFFERENCE"] =
-        dispatch["MAXIMUM"] = &TFLiteImporter::parseEltwise;
+        dispatch["MAXIMUM"] = dispatch["MINIMUM"]= &TFLiteImporter::parseEltwise;
     dispatch["RELU"] = dispatch["PRELU"] = dispatch["HARD_SWISH"] =
         dispatch["LOGISTIC"] = dispatch["LEAKY_RELU"] = &TFLiteImporter::parseActivation;
     dispatch["MAX_POOL_2D"] = dispatch["AVERAGE_POOL_2D"] = &TFLiteImporter::parsePooling;
@@ -581,6 +581,9 @@ void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode,
     }
     else if (opcode == "MAXIMUM" && !isOpInt8) {
         layerParams.set("operation", "max");
+    }
+    else if (opcode == "MINIMUM" && !isOpInt8) {
+        layerParams.set("operation", "min");
     }else {
         CV_Error(Error::StsNotImplemented, cv::format("DNN/TFLite: Unknown opcode for %s Eltwise layer '%s'", isOpInt8 ? "INT8" : "FP32", opcode.c_str()));
     }
diff --git a/modules/dnn/test/test_tflite_importer.cpp b/modules/dnn/test/test_tflite_importer.cpp
index 3cee776611..f7689405dc 100644
--- a/modules/dnn/test/test_tflite_importer.cpp
+++ b/modules/dnn/test/test_tflite_importer.cpp
@@ -311,6 +311,34 @@ TEST_P(Test_TFLite, maximum)
     normAssert(ref, out, "", l1, lInf);
 }
 
+TEST_P(Test_TFLite, minimum)
+{
+    Net net = readNetFromTFLite(findDataFile("dnn/tflite/minimum.tflite"));
+
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
+    Mat input_x = blobFromNPY(findDataFile("dnn/tflite/minimum_input_x.npy"));
+    Mat input_y = blobFromNPY(findDataFile("dnn/tflite/minimum_input_y.npy"));
+
+    net.setInput(input_x, "x");
+    net.setInput(input_y, "y");
+
+    Mat out = net.forward();
+    Mat ref = blobFromNPY(findDataFile("dnn/tflite/minimum_output.npy"));
+
+    double l1 = 1e-5;
+    double lInf = 1e-4;
+
+    if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 1e-3;
+        lInf = 1e-3;
+    }
+
+    normAssert(ref, out, "", l1, lInf);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_TFLite, dnnBackendsAndTargets());
 
 }}  // namespace