diff --git a/README.md b/README.md
index 7634daf6..6b98f506 100644
--- a/README.md
+++ b/README.md
@@ -413,7 +413,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
   * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
 
   * recalculate anchors for your dataset for `width` and `height` from cfg-file:
-  `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 416`
+  `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
    then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file
 
   * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
diff --git a/build/darknet/x64/calc_anchors.cmd b/build/darknet/x64/calc_anchors.cmd
index 1c7d2d8f..45bb6ff9 100644
--- a/build/darknet/x64/calc_anchors.cmd
+++ b/build/darknet/x64/calc_anchors.cmd
@@ -1,10 +1,10 @@
 rem # How to calculate Yolo v2 anchors using K-means++
 
 
-darknet.exe detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -heigh 416
+darknet.exe detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416
 
 
-rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -heigh 416 -show
+rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 -show
 
 
 
diff --git a/src/box.c b/src/box.c
index 00e3edbf..a2c676f2 100644
--- a/src/box.c
+++ b/src/box.c
@@ -294,7 +294,6 @@ int nms_comparator_v3(const void *pa, const void *pb)
 
 void do_nms_obj(detection *dets, int total, int classes, float thresh)
 {
-	printf(" total = %d, classes = %d, thresh = %f \n", total, classes, thresh);
 	int i, j, k;
 	k = total - 1;
 	for (i = 0; i <= k; ++i) {
diff --git a/src/detector.c b/src/detector.c
index 766819f1..082b7489 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -1107,7 +1107,7 @@ void run_detector(int argc, char **argv)
     int frame_skip = find_int_arg(argc, argv, "-s", 0);
 	int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
 	int width = find_int_arg(argc, argv, "-width", 13);
-	int heigh = find_int_arg(argc, argv, "-heigh", 13);
+	int height = find_int_arg(argc, argv, "-height", 13);
     if(argc < 4){
         fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
         return;
@@ -1148,7 +1148,7 @@ void run_detector(int argc, char **argv)
     else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
     else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
 	else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
-	else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, heigh, show);
+	else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
     else if(0==strcmp(argv[2], "demo")) {
         list *options = read_data_cfg(datacfg);
         int classes = option_find_int(options, "classes", 20);