diff --git a/src/detector.c b/src/detector.c
index 373fac2f..b0e0d85d 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -35,9 +35,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_
 
 int check_mistakes;
 
-static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
-
-float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
+static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 };
 
 void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map)
 {
@@ -46,20 +44,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     char *valid_images = option_find_str(options, "valid", train_images);
     char *backup_directory = option_find_str(options, "backup", "/backup/");
 
-    int train_images_num = 0;
-    if (calc_map) {
-        FILE* valid_file = fopen(valid_images, "r");
-        if (!valid_file) {
-            printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
-            getchar();
-            exit(-1);
-        }
-        else fclose(valid_file);
-        list *plist = get_paths(train_images);
-        train_images_num = plist->size;
-        free_list(plist);
-    }
-
     srand(time(0));
     char *base = basecfg(cfgfile);
     printf("%s\n", base);
@@ -69,28 +53,65 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     srand(time(0));
     int seed = rand();
     int i;
-    for(i = 0; i < ngpus; ++i){
+    for (i = 0; i < ngpus; ++i) {
         srand(seed);
 #ifdef GPU
         cuda_set_device(gpus[i]);
 #endif
         nets[i] = parse_network_cfg(cfgfile);
-        if(weightfile){
+        if (weightfile) {
             load_weights(&nets[i], weightfile);
         }
-        if(clear) *nets[i].seen = 0;
+        if (clear) *nets[i].seen = 0;
         nets[i].learning_rate *= ngpus;
     }
     srand(time(0));
     network net = nets[0];
 
+    int train_images_num = 0;
+    network net_map;
+    if (calc_map) {
+        FILE* valid_file = fopen(valid_images, "r");
+        if (!valid_file) {
+            printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
+            getchar();
+            exit(-1);
+        }
+        else fclose(valid_file);
+        list *plist = get_paths(train_images);
+        train_images_num = plist->size;
+        free_list(plist);
+        printf(" Prepare additional network for mAP calculation...\n");
+        net_map = parse_network_cfg_custom(cfgfile, 1);
+
+        int k;
+        for (k = 0; k < net.n; ++k) {
+            layer l = net.layers[k];
+            if (l.type == CONVOLUTIONAL) {
+                net_map.layers[k].biases = l.biases;
+                net_map.layers[k].scales = l.scales;
+                net_map.layers[k].rolling_mean = l.rolling_mean;
+                net_map.layers[k].rolling_variance = l.rolling_variance;
+                net_map.layers[k].weights = l.weights;
+
+                net_map.layers[k].biases_gpu = l.biases_gpu;
+                net_map.layers[k].scales_gpu = l.scales_gpu;
+                net_map.layers[k].rolling_mean_gpu = l.rolling_mean_gpu;
+                net_map.layers[k].rolling_variance_gpu = l.rolling_variance_gpu;
+                net_map.layers[k].weights_gpu = l.weights_gpu;
+                net_map.layers[k].weights_gpu16 = l.weights_gpu16;
+            }
+        }
+    }
+
+
     const int actual_batch_size = net.batch * net.subdivisions;
     if (actual_batch_size == 1) {
         printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");
         getchar();
     }
     else if (actual_batch_size < 64) {
-            printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);
+        printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);
     }
 
     int imgs = net.batch * net.subdivisions * ngpus;
@@ -114,7 +135,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     iter_map = get_current_batch(net);
     float mean_average_precision = -1;
 
-    load_args args = {0};
+    load_args args = { 0 };
     args.w = net.w;
     args.h = net.h;
     args.c = net.c;
@@ -137,7 +158,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
 #ifdef OPENCV
     args.threads = 3 * ngpus;   // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge
-    //args.threads = 12 * ngpus;    // Ryzen 7 2700X (16 logical cores)
+                                //args.threads = 12 * ngpus;    // Ryzen 7 2700X (16 logical cores)
     IplImage* img = NULL;
     float max_img_loss = 5;
     int number_of_lines = 100;
@@ -150,8 +171,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     double time;
     int count = 0;
     //while(i*imgs < N*120){
-    while(get_current_batch(net) < net.max_batches){
-        if(l.random && count++%10 == 0){
+    while (get_current_batch(net) < net.max_batches) {
+        if (l.random && count++ % 10 == 0) {
             printf("Resizing\n");
             //int dim = (rand() % 12 + (init_w/32 - 5)) * 32;    // +-160
             //int dim = (rand() % 4 + 16) * 32;
@@ -177,41 +198,42 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             free_data(train);
             load_thread = load_data(args);
 
-            for(i = 0; i < ngpus; ++i){
+            for (i = 0; i < ngpus; ++i) {
                 resize_network(nets + i, dim_w, dim_h);
             }
             net = nets[0];
         }
-        time=what_time_is_it_now();
+        time = what_time_is_it_now();
         pthread_join(load_thread, 0);
         train = buffer;
         load_thread = load_data(args);
 
         /*
-           int k;
-           for(k = 0; k < l.max_boxes; ++k){
-           box b = float_to_box(train.y.vals[10] + 1 + k*5);
-           if(!b.x) break;
-           printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
-           }
-           image im = float_to_image(448, 448, 3, train.X.vals[10]);
-           int k;
-           for(k = 0; k < l.max_boxes; ++k){
-           box b = float_to_box(train.y.vals[10] + 1 + k*5);
-           printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
-           draw_bbox(im, b, 8, 1,0,0);
-           }
-           save_image(im, "truth11");
-         */
-
-        printf("Loaded: %lf seconds\n", (what_time_is_it_now()-time));
-
-        time=what_time_is_it_now();
+        int k;
+        for(k = 0; k < l.max_boxes; ++k){
+        box b = float_to_box(train.y.vals[10] + 1 + k*5);
+        if(!b.x) break;
+        printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
+        }
+        image im = float_to_image(448, 448, 3, train.X.vals[10]);
+        int k;
+        for(k = 0; k < l.max_boxes; ++k){
+        box b = float_to_box(train.y.vals[10] + 1 + k*5);
+        printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
+        draw_bbox(im, b, 8, 1,0,0);
+        }
+        save_image(im, "truth11");
+        */
+
+        printf("Loaded: %lf seconds\n", (what_time_is_it_now() - time));
+
+        time = what_time_is_it_now();
         float loss = 0;
 #ifdef GPU
-        if(ngpus == 1){
+        if (ngpus == 1) {
             loss = train_network(net, train);
-        } else {
+        }
+        else {
             loss = train_networks(nets, ngpus, train, 4);
         }
 #else
@@ -222,31 +244,18 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
         i = get_current_batch(net);
         if (net.cudnn_half) {
-            if (i < net.burn_in*3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3*net.burn_in);
+            if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
             else printf("\n Tensor Cores are used.");
         }
-        printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);
+        printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs);
 
 #ifdef OPENCV
         if (!dont_show) {
             int draw_precision = 0;
             int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);
             if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {
-                if (l.random) {
-                    printf("Resizing to initial size: %d x %d \n", init_w, init_h);
-                    args.w = init_w;
-                    args.h = init_h;
-                    pthread_join(load_thread, 0);
-                    train = buffer;
-                    load_thread = load_data(args);
-                    int k;
-                    for (k = 0; k < ngpus; ++k) {
-                        resize_network(nets + k, init_w, init_h);
-                    }
-                    net = nets[0];
-                }
                 iter_map = i;
-                mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net);
+                mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map);
                 printf("\n mean_average_precision = %f \n", mean_average_precision);
                 draw_precision = 1;
             }
@@ -257,7 +266,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
         //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
         //if (i % 100 == 0) {
-        if(i >= (iter_save + 1000)) {
+        if (i >= (iter_save + 1000)) {
             iter_save = i;
 #ifdef GPU
             if (ngpus != 1) sync_nets(nets, ngpus, 0);
@@ -279,7 +288,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         free_data(train);
     }
 #ifdef GPU
-    if(ngpus != 1) sync_nets(nets, ngpus, 0);
+    if (ngpus != 1) sync_nets(nets, ngpus, 0);
 #endif
     char buff[256];
     sprintf(buff, "%s/%s_final.weights", backup_directory, base);
@@ -632,8 +641,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
         char *train_images = option_find_str(options, "train", "data/train.txt");
         char *valid_images = option_find_str(options, "valid", train_images);
         net = *existing_net;
-        initial_batch = net.batch;
-        set_batch_network(&net, 1);
     }
     else {
         net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
@@ -810,12 +817,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
                         // calc avg IoU, true-positives, false-positives for required Threshold
                         if (prob > thresh_calc_avg_iou) {
                             int z, found = 0;
-                            for (z = checkpoint_detections_count; z < detections_count-1; ++z)
+                            for (z = checkpoint_detections_count; z < detections_count - 1; ++z)
                                 if (detections[z].unique_truth_index == truth_index) {
                                     found = 1; break;
                                 }
 
-                            if(truth_index > -1 && found == 0) {
+                            if (truth_index > -1 && found == 0) {
                                 avg_iou += max_iou;
                                 ++tp_for_thresh;
                             }
@@ -844,7 +851,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
         }
     }
 
-    if((tp_for_thresh + fp_for_thresh) > 0)
+    if ((tp_for_thresh + fp_for_thresh) > 0)
         avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
 
 
@@ -869,7 +876,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
 
     int rank;
     for (rank = 0; rank < detections_count; ++rank) {
-        if(rank % 100 == 0)
+        if (rank % 100 == 0)
             printf(" rank = %d of ranks = %d \r", rank, detections_count);
 
         if (rank > 0) {
@@ -932,7 +939,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
             avg_precision += cur_precision;
         }
         avg_precision = avg_precision / 11;
-        printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100);
+        printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision * 100);
         mean_average_precision += avg_precision;
     }
 
@@ -970,7 +977,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
     free_list(options);
 
     if (existing_net) {
-        set_batch_network(&net, initial_batch);
+        //set_batch_network(&net, initial_batch);
     }
     else {
         free_network(net);
@@ -998,7 +1005,7 @@ int anchors_data_comparator(const float **pa, const float **pb)
 {
     float *a = (float *)*pa;
     float *b = (float *)*pb;
-    float diff = b[0]*b[1] - a[0]*a[1];
+    float diff = b[0] * b[1] - a[0] * a[1];
     if (diff < 0) return 1;
     else if (diff > 0) return -1;
     return 0;
@@ -1054,7 +1061,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int
             rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
             rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
             rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
-            printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes);
+            printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes);
         }
     }
     printf("\n all loaded. \n");
@@ -1086,7 +1093,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int
     for (i = 0; i < number_of_boxes; ++i) {
         float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2];
         float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1];
-        //int cluster_idx = labels->data.i[i];
+                                                         //int cluster_idx = labels->data.i[i];
         int cluster_idx = 0;
         float min_dist = FLT_MAX;
         float best_iou = 0;
@@ -1121,7 +1128,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int
         for (i = 0; i < num_of_clusters; ++i) {
             float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2];
             float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1];
-            if(width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h);
+            if (width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h);
             else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h);
             printf("%s", buff);
             fwrite(buff, sizeof(char), strlen(buff), fw);
@@ -1201,7 +1208,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int
 //#endif // OPENCV
 
 void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
-                   float hier_thresh, int dont_show, int ext_output, int save_labels)
+    float hier_thresh, int dont_show, int ext_output, int save_labels)
 {
     list *options = read_data_cfg(datacfg);
     char *name_list = option_find_str(options, "names", "data/names.list");
@@ -1210,7 +1217,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
 
     image **alphabet = load_alphabet();
     network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
-    if(weightfile){
+    if (weightfile) {
         load_weights(&net, weightfile);
     }
     //set_batch_network(&net, 1);
@@ -1219,31 +1226,32 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
     if (net.layers[net.n - 1].classes != names_size) {
         printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
             name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
-        if(net.layers[net.n - 1].classes > names_size) getchar();
+        if (net.layers[net.n - 1].classes > names_size) getchar();
     }
     srand(2222222);
     double time;
     char buff[256];
     char *input = buff;
     int j;
-    float nms=.45;    // 0.4F
-    while(1){
-        if(filename){
+    float nms = .45;    // 0.4F
+    while (1) {
+        if (filename) {
             strncpy(input, filename, 256);
-            if(strlen(input) > 0)
+            if (strlen(input) > 0)
                 if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
-        } else {
+        }
+        else {
             printf("Enter Image Path: ");
             fflush(stdout);
             input = fgets(input, 256, stdin);
-            if(!input) return;
+            if (!input) return;
             strtok(input, "\n");
         }
-        image im = load_image(input,0,0,net.c);
+        image im = load_image(input, 0, 0, net.c);
         int letterbox = 0;
         image sized = resize_image(im, net.w, net.h);
         //image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
-        layer l = net.layers[net.n-1];
+        layer l = net.layers[net.n - 1];
 
         //box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
         //float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
@@ -1268,7 +1276,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
         }
 
         // pseudo labeling concept - fast.ai
-        if(save_labels)
+        if (save_labels)
         {
             char labelpath[4096];
             replace_image_to_label(input, labelpath);
@@ -1347,7 +1355,7 @@ void run_detector(int argc, char **argv)
     // and for recall mode (extended output table-like format with results for best_class fit)
     int ext_output = find_arg(argc, argv, "-ext_output");
     int save_labels = find_arg(argc, argv, "-save_labels");
-    if(argc < 4){
+    if (argc < 4) {
         fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]);
         return;
     }
@@ -1355,20 +1363,21 @@ void run_detector(int argc, char **argv)
     int *gpus = 0;
     int gpu = 0;
     int ngpus = 0;
-    if(gpu_list){
+    if (gpu_list) {
         printf("%s\n", gpu_list);
         int len = strlen(gpu_list);
         ngpus = 1;
         int i;
-        for(i = 0; i < len; ++i){
+        for (i = 0; i < len; ++i) {
             if (gpu_list[i] == ',') ++ngpus;
         }
         gpus = calloc(ngpus, sizeof(int));
-        for(i = 0; i < ngpus; ++i){
+        for (i = 0; i < ngpus; ++i) {
             gpus[i] = atoi(gpu_list);
-            gpu_list = strchr(gpu_list, ',')+1;
+            gpu_list = strchr(gpu_list, ',') + 1;
         }
-    } else {
+    }
+    else {
         gpu = gpu_index;
         gpus = &gpu;
         ngpus = 1;
@@ -1379,23 +1388,23 @@ void run_detector(int argc, char **argv)
     char *datacfg = argv[3];
     char *cfg = argv[4];
     char *weights = (argc > 5) ? argv[5] : 0;
-    if(weights)
-        if(strlen(weights) > 0)
+    if (weights)
+        if (strlen(weights) > 0)
             if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
-    char *filename = (argc > 6) ? argv[6]: 0;
-    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
-    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map);
-    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, iou_thresh, NULL);
-    else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
-    else if(0==strcmp(argv[2], "demo")) {
+    char *filename = (argc > 6) ? argv[6] : 0;
+    if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
+    else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map);
+    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, iou_thresh, NULL);
+    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);
         char *name_list = option_find_str(options, "names", "data/names.list");
         char **names = get_labels(name_list);
-        if(filename)
-            if(strlen(filename) > 0)
+        if (filename)
+            if (strlen(filename) > 0)
                 if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
         demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
             http_stream_port, dont_show, ext_output);
diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c
index b0874304..8c1265e3 100644
--- a/src/maxpool_layer.c
+++ b/src/maxpool_layer.c
@@ -104,6 +104,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h)
     l->indexes_gpu = cuda_make_int_array(output_size);
     l->output_gpu  = cuda_make_array(l->output, output_size);
     l->delta_gpu   = cuda_make_array(l->delta,  output_size);
+
+    cudnn_maxpool_setup(l);
     #endif
 }
 
diff --git a/src/network.h b/src/network.h
index 65029947..9acb01da 100644
--- a/src/network.h
+++ b/src/network.h
@@ -145,6 +145,7 @@ YOLODLL_API void reset_rnn(network *net);
 YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch);
 YOLODLL_API network *load_network(char *cfg, char *weights, int clear);
 YOLODLL_API float *network_predict_image(network *net, image im);
+YOLODLL_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
 YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map);
 YOLODLL_API int network_width(network *net);
 YOLODLL_API int network_height(network *net);