stable

Makefile

@@ -34,7 +34,7 @@ CFLAGS+= -DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o yoloplus.o
 ifeq ($(GPU), 1) 
 OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif

cfg/darknet.cfg

@@ -27,7 +27,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -38,7 +38,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -49,7 +49,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -60,7 +60,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -71,7 +71,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -82,7 +82,7 @@ pad=1
 activation=leaky
 
 [maxpool]
-size=3
+size=2
 stride=2
 
 [convolutional]
@@ -99,7 +99,7 @@ probability=.5
 
 [connected]
 output=1000
-activation=linear
+activation=leaky
 
 [softmax]
 

cfg/yolo.cfg

@@ -4,10 +4,15 @@ subdivisions=64
 height=448
 width=448
 channels=3
-learning_rate=0.01
+learning_rate=0.001
 momentum=0.9
 decay=0.0005
 
+policy=steps
+steps=50, 5000
+scales=10, .1
+max_batches = 8000
+
 [crop]
 crop_width=448
 crop_height=448

src/darknet.c

@@ -13,6 +13,7 @@
 
 extern void run_imagenet(int argc, char **argv);
 extern void run_yolo(int argc, char **argv);
+extern void run_yoloplus(int argc, char **argv);
 extern void run_coco(int argc, char **argv);
 extern void run_writing(int argc, char **argv);
 extern void run_captcha(int argc, char **argv);
@@ -178,6 +179,8 @@ int main(int argc, char **argv)
         average(argc, argv);
     } else if (0 == strcmp(argv[1], "yolo")){
         run_yolo(argc, argv);
+    } else if (0 == strcmp(argv[1], "yoloplus")){
+        run_yoloplus(argc, argv);
     } else if (0 == strcmp(argv[1], "coco")){
         run_coco(argc, argv);
     } else if (0 == strcmp(argv[1], "compare")){

src/detection_layer.c

@@ -85,11 +85,12 @@ void forward_detection_layer(const detection_layer l, network_state state)
         int size = get_detection_layer_output_size(l) * l.batch;
         memset(l.delta, 0, size * sizeof(float));
         for (i = 0; i < l.batch*locations; ++i) {
-            int classes = l.objectness+l.classes;
+            int classes = (l.objectness || l.background)+l.classes;
             int offset = i*(classes+l.coords);
             for (j = offset; j < offset+classes; ++j) {
                 *(l.cost) += pow(state.truth[j] - l.output[j], 2);
                 l.delta[j] =  state.truth[j] - l.output[j];
+                if(l.background && j == offset) l.delta[j] *= .1;
             }
 
             box truth;
@@ -115,9 +116,15 @@ void forward_detection_layer(const detection_layer l, network_state state)
             l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
             l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
             if(l.rescore){
-                for (j = offset; j < offset+classes; ++j) {
-                    if(state.truth[j]) state.truth[j] = iou;
-                    l.delta[j] =  state.truth[j] - l.output[j];
+                if(l.objectness){
+                    state.truth[offset] = iou;
+                    l.delta[offset] = state.truth[offset] - l.output[offset];
+                }
+                else{
+                    for (j = offset; j < offset+classes; ++j) {
+                        if(state.truth[j]) state.truth[j] = iou;
+                        l.delta[j] =  state.truth[j] - l.output[j];
+                    }
                 }
             }
         }
@@ -145,7 +152,7 @@ void backward_detection_layer(const detection_layer l, network_state state)
         if (l.objectness) {
 
         }else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i);
-        for(j = 0; j < l.coords; ++j){
+        for (j = 0; j < l.coords; ++j){
             state.delta[in_i++] += l.delta[out_i++];
         }
         if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] += latent_delta;

src/network.c

@@ -29,15 +29,26 @@ int get_current_batch(network net)
 float get_current_rate(network net)
 {
     int batch_num = get_current_batch(net);
+    int i;
+    float rate;
     switch (net.policy) {
         case CONSTANT:
             return net.learning_rate;
         case STEP:
-            return net.learning_rate * pow(net.gamma, batch_num/net.step);
+            return net.learning_rate * pow(net.scale, batch_num/net.step);
+        case STEPS:
+            rate = net.learning_rate;
+            for(i = 0; i < net.num_steps; ++i){
+                if(net.steps[i] > batch_num) return rate;
+                rate *= net.scales[i];
+            }
+            return rate;
         case EXP:
             return net.learning_rate * pow(net.gamma, batch_num);
         case POLY:
             return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+        case SIG:
+            return net.learning_rate * (1/(1+exp(net.gamma*(batch_num - net.step))));
         default:
             fprintf(stderr, "Policy is weird!\n");
             return net.learning_rate;

src/network.h

@@ -8,7 +8,7 @@
 #include "data.h"
 
 typedef enum {
-    CONSTANT, STEP, EXP, POLY
+    CONSTANT, STEP, EXP, POLY, STEPS, SIG
 } learning_rate_policy;
 
 typedef struct {
@@ -25,9 +25,13 @@ typedef struct {
 
     float learning_rate;
     float gamma;
+    float scale;
     float power;
     int step;
     int max_batches;
+    float *scales;
+    int   *steps;
+    int num_steps;
 
     int inputs;
     int h, w, c;

src/parser.c

@@ -169,7 +169,7 @@ detection_layer parse_detection(list *options, size_params params)
     int rescore = option_find_int(options, "rescore", 0);
     int joint = option_find_int(options, "joint", 0);
     int objectness = option_find_int(options, "objectness", 0);
-    int background = 0;
+    int background = option_find_int(options, "background", 0);
     detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness);
     return layer;
 }
@@ -312,6 +312,8 @@ learning_rate_policy get_policy(char *s)
     if (strcmp(s, "constant")==0) return CONSTANT;
     if (strcmp(s, "step")==0) return STEP;
     if (strcmp(s, "exp")==0) return EXP;
+    if (strcmp(s, "sigmoid")==0) return SIG;
+    if (strcmp(s, "steps")==0) return STEPS;
     fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
     return CONSTANT;
 }
@@ -337,9 +339,36 @@ void parse_net_options(list *options, network *net)
     net->policy = get_policy(policy_s);
     if(net->policy == STEP){
         net->step = option_find_int(options, "step", 1);
-        net->gamma = option_find_float(options, "gamma", 1);
+        net->scale = option_find_float(options, "scale", 1);
+    } else if (net->policy == STEPS){
+        char *l = option_find(options, "steps");   
+        char *p = option_find(options, "scales");   
+        if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
+
+        int len = strlen(l);
+        int n = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (l[i] == ',') ++n;
+        }
+        int *steps = calloc(n, sizeof(int));
+        float *scales = calloc(n, sizeof(float));
+        for(i = 0; i < n; ++i){
+            int step    = atoi(l);
+            float scale = atof(p);
+            l = strchr(l, ',')+1;
+            p = strchr(p, ',')+1;
+            steps[i] = step;
+            scales[i] = scale;
+        }
+        net->scales = scales;
+        net->steps = steps;
+        net->num_steps = n;
     } else if (net->policy == EXP){
         net->gamma = option_find_float(options, "gamma", 1);
+    } else if (net->policy == SIG){
+        net->gamma = option_find_float(options, "gamma", 1);
+        net->step = option_find_int(options, "step", 1);
     } else if (net->policy == POLY){
         net->power = option_find_float(options, "power", 1);
     }
@@ -401,10 +430,10 @@ network parse_network_cfg(char *filename)
             l = parse_dropout(options, params);
             l.output = net.layers[count-1].output;
             l.delta = net.layers[count-1].delta;
-            #ifdef GPU
+#ifdef GPU
             l.output_gpu = net.layers[count-1].output_gpu;
             l.delta_gpu = net.layers[count-1].delta_gpu;
-            #endif
+#endif
         }else{
             fprintf(stderr, "Type not recognized: %s\n", s->type);
         }

src/yolo.c

@@ -66,7 +66,6 @@ void train_yolo(char *cfgfile, char *weightfile)
         load_weights(&net, weightfile);
     }
     detection_layer layer = get_network_detection_layer(net);
-    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 128;
     int i = *net.seen/imgs;
 
@@ -75,10 +74,6 @@ void train_yolo(char *cfgfile, char *weightfile)
     int N = plist->size;
     paths = (char **)list_to_array(plist);
 
-    if(i*imgs > N*80){
-        net.layers[net.n-1].joint = 1;
-        net.layers[net.n-1].objectness = 0;
-    }
     if(i*imgs > N*120){
         net.layers[net.n-1].rescore = 1;
     }
@@ -102,7 +97,7 @@ void train_yolo(char *cfgfile, char *weightfile)
 
     pthread_t load_thread = load_data_in_thread(args);
     clock_t time;
-    while(i*imgs < N*130){
+    while(get_current_batch(net) < net.max_batches){
         i += 1;
         time=clock();
         pthread_join(load_thread, 0);
@@ -115,19 +110,10 @@ void train_yolo(char *cfgfile, char *weightfile)
         if (avg_loss < 0) avg_loss = loss;
         avg_loss = avg_loss*.9 + loss*.1;
 
-        printf("%d: %f, %f avg, %lf seconds, %d images, epoch: %f\n", i, loss, avg_loss, sec(clock()-time), i*imgs, ((float)i)*imgs/N);
-
-        if((i-1)*imgs <= N && i*imgs > N){
-            fprintf(stderr, "First stage done\n");
-            net.learning_rate *= 10;
-            char buff[256];
-            sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
-            save_weights(net, buff);
-        }
+        printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N);
 
         if((i-1)*imgs <= 80*N && i*imgs > N*80){
             fprintf(stderr, "Second stage done.\n");
-            net.learning_rate *= .1;
             char buff[256];
             sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
             save_weights(net, buff);

src/yoloplus.c

+#include "network.h"
+#include "detection_layer.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "box.h"
+
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
+
+char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+
+void draw_yoloplus(image im, float *box, int side, int objectness, char *label, float thresh)
+{
+    int classes = 20;
+    int elems = 4+classes+objectness;
+    int j;
+    int r, c;
+
+    for(r = 0; r < side; ++r){
+        for(c = 0; c < side; ++c){
+            j = (r*side + c) * elems;
+            float scale = 1;
+            if(objectness) scale = 1 - box[j++];
+            int class = max_index(box+j, classes);
+            if(scale * box[j+class] > thresh){
+                int width = sqrt(scale*box[j+class])*5 + 1;
+                printf("%f %s\n", scale * box[j+class], voc_names[class]);
+                float red = get_color(0,class,classes);
+                float green = get_color(1,class,classes);
+                float blue = get_color(2,class,classes);
+
+                j += classes;
+                float x = box[j+0];
+                float y = box[j+1];
+                x = (x+c)/side;
+                y = (y+r)/side;
+                float w = box[j+2]; //*maxwidth;
+                float h = box[j+3]; //*maxheight;
+                h = h*h;
+                w = w*w;
+
+                int left  = (x-w/2)*im.w;
+                int right = (x+w/2)*im.w;
+                int top   = (y-h/2)*im.h;
+                int bot   = (y+h/2)*im.h;
+                draw_box_width(im, left, top, right, bot, width, red, green, blue);
+            }
+        }
+    }
+    show_image(im, label);
+}
+
+void train_yoloplus(char *cfgfile, char *weightfile)
+{
+    char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+    char *backup_directory = "/home/pjreddie/backup/";
+    srand(time(0));
+    data_seed = time(0);
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    detection_layer layer = get_network_detection_layer(net);
+    int imgs = 128;
+    int i = *net.seen/imgs;
+
+    char **paths;
+    list *plist = get_paths(train_images);
+    int N = plist->size;
+    paths = (char **)list_to_array(plist);
+
+    if(i*imgs > N*120){
+        net.layers[net.n-1].rescore = 1;
+    }
+    data train, buffer;
+
+    int classes = layer.classes;
+    int background = layer.objectness;
+    int side = sqrt(get_detection_layer_locations(layer));
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.paths = paths;
+    args.n = imgs;
+    args.m = plist->size;
+    args.classes = classes;
+    args.num_boxes = side;
+    args.background = background;
+    args.d = &buffer;
+    args.type = DETECTION_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    clock_t time;
+    while(get_current_batch(net) < net.max_batches){
+        i += 1;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_in_thread(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        if (avg_loss < 0) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N);
+
+        if((i-1)*imgs <= 80*N && i*imgs > N*80){
+            fprintf(stderr, "Second stage done.\n");
+            char buff[256];
+            sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
+            save_weights(net, buff);
+            net.layers[net.n-1].joint = 1;
+            net.layers[net.n-1].objectness = 0;
+            background = 0;
+
+            pthread_join(load_thread, 0);
+            free_data(buffer);
+            args.background = background;
+            load_thread = load_data_in_thread(args);
+        }
+
+        if((i-1)*imgs <= 120*N && i*imgs > N*120){
+            fprintf(stderr, "Third stage done.\n");
+            char buff[256];
+            sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+            net.layers[net.n-1].rescore = 1;
+            save_weights(net, buff);
+        }
+
+        if(i%1000==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s_rescore.weights", backup_directory, base);
+    save_weights(net, buff);
+}
+
+void convert_yoloplus_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
+{
+    int i,j;
+    int per_box = 4+classes+(background || objectness);
+    for (i = 0; i < num_boxes*num_boxes; ++i){
+        float scale = 1;
+        if(objectness) scale = 1-predictions[i*per_box];
+        int offset = i*per_box+(background||objectness);
+        for(j = 0; j < classes; ++j){
+            float prob = scale*predictions[offset+j];
+            probs[i][j] = (prob > thresh) ? prob : 0;
+        }
+        int row = i / num_boxes;
+        int col = i % num_boxes;
+        offset += classes;
+        boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
+        boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
+        boxes[i].w = pow(predictions[offset + 2], 2) * w;
+        boxes[i].h = pow(predictions[offset + 3], 2) * h;
+    }
+}
+
+void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+{
+    int i, j;
+    for(i = 0; i < num_boxes*num_boxes; ++i){
+        float xmin = boxes[i].x - boxes[i].w/2.;
+        float xmax = boxes[i].x + boxes[i].w/2.;
+        float ymin = boxes[i].y - boxes[i].h/2.;
+        float ymax = boxes[i].y + boxes[i].h/2.;
+
+        if (xmin < 0) xmin = 0;
+        if (ymin < 0) ymin = 0;
+        if (xmax > w) xmax = w;
+        if (ymax > h) ymax = h;
+
+        for(j = 0; j < classes; ++j){
+            if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
+                    xmin, ymin, xmax, ymax);
+        }
+    }
+}
+
+void validate_yoloplus(char *cfgfile, char *weightfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    detection_layer layer = get_network_detection_layer(net);
+    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    srand(time(0));
+
+    char *base = "results/comp4_det_test_";
+    list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+    char **paths = (char **)list_to_array(plist);
+
+    int classes = layer.classes;
+    int objectness = layer.objectness;
+    int background = layer.background;
+    int num_boxes = sqrt(get_detection_layer_locations(layer));
+
+    int j;
+    FILE **fps = calloc(classes, sizeof(FILE *));
+    for(j = 0; j < classes; ++j){
+        char buff[1024];
+        snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
+        fps[j] = fopen(buff, "w");
+    }
+    box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
+    float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
+    for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
+
+    int m = plist->size;
+    int i=0;
+    int t;
+
+    float thresh = .001;
+    int nms = 1;
+    float iou_thresh = .5;
+
+    int nthreads = 8;
+    image *val = calloc(nthreads, sizeof(image));
+    image *val_resized = calloc(nthreads, sizeof(image));
+    image *buf = calloc(nthreads, sizeof(image));
+    image *buf_resized = calloc(nthreads, sizeof(image));
+    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.type = IMAGE_DATA;
+
+    for(t = 0; t < nthreads; ++t){
+        args.path = paths[i+t];
+        args.im = &buf[t];
+        args.resized = &buf_resized[t];
+        thr[t] = load_data_in_thread(args);
+    }
+    time_t start = time(0);
+    for(i = nthreads; i < m+nthreads; i += nthreads){
+        fprintf(stderr, "%d\n", i);
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            pthread_join(thr[t], 0);
+            val[t] = buf[t];
+            val_resized[t] = buf_resized[t];
+        }
+        for(t = 0; t < nthreads && i+t < m; ++t){
+            args.path = paths[i+t];
+            args.im = &buf[t];
+            args.resized = &buf_resized[t];
+            thr[t] = load_data_in_thread(args);
+        }
+        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+            char *path = paths[i+t-nthreads];
+            char *id = basecfg(path);
+            float *X = val_resized[t].data;
+            float *predictions = network_predict(net, X);
+            int w = val[t].w;
+            int h = val[t].h;
+            convert_yoloplus_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
+            if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh);
+            print_yoloplus_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
+            free(id);
+            free_image(val[t]);
+            free_image(val_resized[t]);
+        }
+    }
+    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+}
+
+void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh)
+{
+
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    detection_layer layer = get_network_detection_layer(net);
+    set_batch_network(&net, 1);
+    srand(2222222);
+    clock_t time;
+    char input[256];
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        } else {
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            fgets(input, 256, stdin);
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input,0,0);
+        image sized = resize_image(im, net.w, net.h);
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        draw_yoloplus(im, predictions, 7, layer.objectness, "predictions", thresh);
+        free_image(im);
+        free_image(sized);
+#ifdef OPENCV
+        cvWaitKey(0);
+        cvDestroyAllWindows();
+#endif
+        if (filename) break;
+    }
+}
+
+void run_yoloplus(int argc, char **argv)
+{
+    float thresh = find_float_arg(argc, argv, "-thresh", .2);
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *cfg = argv[3];
+    char *weights = (argc > 4) ? argv[4] : 0;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "test")) test_yoloplus(cfg, weights, filename, thresh);
+    else if(0==strcmp(argv[2], "train")) train_yoloplus(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_yoloplus(cfg, weights);
+}