diff --git a/scripts/test_inference.sh b/scripts/test_inference.sh
new file mode 100644
index 0000000..fe8dac3
--- /dev/null
+++ b/scripts/test_inference.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+function test_inference {
+    ./test_$1
+    ./test_rtinference $1_$2.rt 1 
+    ./test_rtinference $1_$2.rt 4
+}
+
+sudo jeston_clock
+
+# modes=( 1 ) # only FP32
+# modes=( 1 2 ) # FP32 and FP16
+modes=( 1 2 3 ) # FP32, FP16 and INT8
+
+rm times_rtinference.csv
+for i in "${modes[@]}"
+do
+    rm *rt
+    if [ $i -eq 1 ]
+    then
+        export TKDNN_MODE=FP32
+        mode=fp32
+        echo -e "${ORANGE}Test FP32${NC}"
+    fi
+    if [ $i -eq 2 ]
+    then
+        export TKDNN_MODE=FP16
+        mode=fp16
+        echo -e "${ORANGE}Test FP16${NC}"
+    fi
+    if [ $i -eq 3 ]
+    then
+        export TKDNN_MODE=INT8
+        export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
+        export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
+        mode=int8
+        echo -e "${ORANGE}Test INT8${NC}"
+        
+	fi
+
+    export TKDNN_BATCHSIZE=4
+    echo -e "${ORANGE}Batch $TKDNN_BATCHSIZE ${NC}"
+    
+    test_inference yolo4_320 $mode
+    test_inference yolo4_416 $mode
+    test_inference yolo4_512 $mode
+    test_inference yolo4_608 $mode
+done
+
+
+
diff --git a/tests/test_rtinference/rtinference.cpp b/tests/test_rtinference/rtinference.cpp
index e128ae8..76c2a33 100644
--- a/tests/test_rtinference/rtinference.cpp
+++ b/tests/test_rtinference/rtinference.cpp
@@ -18,6 +18,8 @@ int main(int argc, char *argv[]) {
 
     //convert network to tensorRT
     tk::dnn::NetworkRT netRT(NULL, argv[1]);
+
+    
     
     tk::dnn::dataDim_t idim = netRT.input_dim;
     tk::dnn::dataDim_t odim = netRT.output_dim;
@@ -63,11 +65,28 @@ int main(int argc, char *argv[]) {
             }
         }
     }
-    std::cout<<"Min: "<<*std::min_element(stats.begin(), stats.end())/BATCH_SIZE<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(stats.begin(), stats.end())/BATCH_SIZE<<" ms\n";    
+
+    double min = *std::min_element(stats.begin(), stats.end())/BATCH_SIZE;
+    double max = *std::max_element(stats.begin(), stats.end())/BATCH_SIZE;
     double mean =0;
     for(int i=0; i<stats.size(); i++) mean += stats[i]; mean /= stats.size();
-    std::cout<<"Avg: "<<mean/BATCH_SIZE<<" ms\t"<<1000/(mean/BATCH_SIZE)<<" FPS\n"<<COL_END;   
+    mean /=BATCH_SIZE;
+
+    std::cout<<"Min: "<<min<<" ms\n";    
+    std::cout<<"Max: "<<max<<" ms\n";    
+    std::cout<<"Avg: "<<mean<<" ms\t"<<1000/(mean)<<" FPS\n"<<COL_END;   
+
+
+    std::ofstream times;
+    times.open("times_rtinference.csv", std::ios_base::app);
+
+    std::string net_name;
+    removePathAndExtension(argv[1], net_name);
+
+    times << net_name<< "_" << BATCH_SIZE << ";" << mean << ";" << min << ";" << max << ";" << 1000./mean << "\n";
+
+    times.close();
+
     return ret_tensorrt;
 }