diff --git a/yolov8/src/model.cpp b/yolov8/src/model.cpp
index c9354220..cd2f8872 100644
--- a/yolov8/src/model.cpp
+++ b/yolov8/src/model.cpp
@@ -1762,7 +1762,7 @@ nvinfer1::IHostMemory* buildEngineYolov8PoseP6(nvinfer1::IBuilder* builder, nvin
                                             (kInputH / strides[0]) * (kInputW / strides[0]), gw, "pose");
     nvinfer1::ITensor* inputTensor30_dfl_0[] = {dfl30_0->getOutput(0), split30_0_1->getOutput(0),
                                                 shuffle_conv20->getOutput(0)};
-    nvinfer1::IConcatenationLayer* cat30_dfl_0 = network->addConcatenation(inputTensor30_dfl_0, 2);
+    nvinfer1::IConcatenationLayer* cat30_dfl_0 = network->addConcatenation(inputTensor30_dfl_0, 3);
 
     // P4 processing steps (remains unchanged)
     nvinfer1::IShuffleLayer* shuffle30_1 =
@@ -1783,7 +1783,7 @@ nvinfer1::IHostMemory* buildEngineYolov8PoseP6(nvinfer1::IBuilder* builder, nvin
                                             (kInputH / strides[1]) * (kInputW / strides[1]), gw, "pose");
     nvinfer1::ITensor* inputTensor30_dfl_1[] = {dfl30_1->getOutput(0), split30_1_1->getOutput(0),
                                                 shuffle_conv23->getOutput(0)};
-    nvinfer1::IConcatenationLayer* cat30_dfl_1 = network->addConcatenation(inputTensor30_dfl_1, 2);
+    nvinfer1::IConcatenationLayer* cat30_dfl_1 = network->addConcatenation(inputTensor30_dfl_1, 3);
 
     // P5 processing steps (remains unchanged)
     nvinfer1::IShuffleLayer* shuffle30_2 =
@@ -1804,7 +1804,7 @@ nvinfer1::IHostMemory* buildEngineYolov8PoseP6(nvinfer1::IBuilder* builder, nvin
                                             (kInputH / strides[2]) * (kInputW / strides[2]), gw, "pose");
     nvinfer1::ITensor* inputTensor30_dfl_2[] = {dfl30_2->getOutput(0), split30_2_1->getOutput(0),
                                                 shuffle_conv26->getOutput(0)};
-    nvinfer1::IConcatenationLayer* cat30_dfl_2 = network->addConcatenation(inputTensor30_dfl_2, 2);
+    nvinfer1::IConcatenationLayer* cat30_dfl_2 = network->addConcatenation(inputTensor30_dfl_2, 3);
 
     // P6 processing steps
     nvinfer1::IShuffleLayer* shuffle30_3 = network->addShuffle(*cat30_3->getOutput(0));
@@ -1819,16 +1819,16 @@ nvinfer1::IHostMemory* buildEngineYolov8PoseP6(nvinfer1::IBuilder* builder, nvin
             DFL(network, weightMap, *split30_3_0->getOutput(0), 4, (kInputH / strides[3]) * (kInputW / strides[3]), 1,
                 1, 0, "model.30.dfl.conv.weight");
 
-    // det2
+    // det3
     auto shuffle_conv29 = cv4_conv_combined(network, weightMap, *conv29->getOutput(0), "model.30.cv4.3",
                                             (kInputH / strides[3]) * (kInputW / strides[3]), gw, "pose");
     nvinfer1::ITensor* inputTensor30_dfl_3[] = {dfl30_3->getOutput(0), split30_3_1->getOutput(0),
                                                 shuffle_conv29->getOutput(0)};
-    nvinfer1::IConcatenationLayer* cat30_dfl_3 = network->addConcatenation(inputTensor30_dfl_3, 2);
+    nvinfer1::IConcatenationLayer* cat30_dfl_3 = network->addConcatenation(inputTensor30_dfl_3, 3);
 
     nvinfer1::IPluginV2Layer* yolo = addYoLoLayer(
             network, std::vector<nvinfer1::IConcatenationLayer*>{cat30_dfl_0, cat30_dfl_1, cat30_dfl_2, cat30_dfl_3},
-            strides, stridesLength, false, false);
+            strides, stridesLength, false, true);
     yolo->getOutput(0)->setName(kOutputTensorName);
     network->markOutput(*yolo->getOutput(0));
 
@@ -1841,8 +1841,8 @@ nvinfer1::IHostMemory* buildEngineYolov8PoseP6(nvinfer1::IBuilder* builder, nvin
     std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
     assert(builder->platformHasFastInt8());
     config->setFlag(nvinfer1::BuilderFlag::kINT8);
-    auto* calibrator =
-            new Int8EntropyCalibrator2(1, kInputW, kInputH, "../coco_calib/", "int8calib.table", kInputTensorName);
+    auto* calibrator = new Int8EntropyCalibrator2(1, kInputW, kInputH, kInputQuantizationFolder, "int8calib.table",
+                                                  kInputTensorName);
     config->setInt8Calibrator(calibrator);
 #endif