diff --git a/yolov5/README.md b/yolov5/README.md
index cdff942..ab4b349 100644
--- a/yolov5/README.md
+++ b/yolov5/README.md
@@ -2,6 +2,8 @@
 
 The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
 
+I was using [commit 6fd3939] of [ultralytics/yolov5](https://github.com/ultralytics/yolov5). And I made a copy of [yolov5s.pt(google drive)](https://drive.google.com/file/d/1w38DgmrP3iwiJi_AOdabuuE_zanMhO5_/view?usp=sharing). Just in case the yolov5 model updated.
+
 ## How to Run
 
 ```
diff --git a/yolov5/yolov5s.cpp b/yolov5/yolov5s.cpp
index 9fc0932..69893f0 100644
--- a/yolov5/yolov5s.cpp
+++ b/yolov5/yolov5s.cpp
@@ -59,7 +59,6 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
     auto cat12 = network->addConcatenation(inputTensors12, 2);
     auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 512, 256, 1, false, 1, 0.5, "model.13");
     auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 128, 1, 1, 1, "model.14");
-    std::cout << "conv14 ----" << std::endl;
 
     Weights deconvwts15{DataType::kFLOAT, deval, 128 * 2 * 2};
     IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 128, DimsHW{2, 2}, deconvwts15, emptywts);
@@ -69,14 +68,12 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
     ITensor* inputTensors16[] = {deconv15->getOutput(0), bottleneck_csp4->getOutput(0)};
     auto cat16 = network->addConcatenation(inputTensors16, 2);
     auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 256, 128, 1, false, 1, 0.5, "model.17");
-    std::cout << "conv18 ----" << std::endl;
     IConvolutionLayer* conv18 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.18.weight"], weightMap["model.18.bias"]);
 
     auto conv19 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 128, 3, 2, 1, "model.19");
     ITensor* inputTensors20[] = {conv19->getOutput(0), conv14->getOutput(0)};
     auto cat20 = network->addConcatenation(inputTensors20, 2);
     auto bottleneck_csp21 = bottleneckCSP(network, weightMap, *cat20->getOutput(0), 256, 256, 1, false, 1, 0.5, "model.21");
-    std::cout << "conv22 ----" << std::endl;
     IConvolutionLayer* conv22 = network->addConvolutionNd(*bottleneck_csp21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.22.weight"], weightMap["model.22.bias"]);
 
     auto conv23 = convBnLeaky(network, weightMap, *bottleneck_csp21->getOutput(0), 256, 3, 2, 1, "model.23");